+
+1. **How do I change the parameters for a rule?**
+
+ - Find the rule you are interested in customizing in `defaults/parameters.yaml`. For example, maybe you want recombinants visualized by `division` rather than `country`.
+
+ ```yaml
+ # ---------------------------------------------------------------------------
+ # geo : Column to use for a geographic summary (typically region, country, or division)
+ - name: linelist
+ geo: country
+ ```
+
+ - Then copy over the defaults into your custom profile (`my_profiles/custom/builds.yaml`), and adjust the yaml formatting. Note that `- name: linelist` has become `linelist:` which is idented to be flush with the `sequences:` parameter.
+
+ ```yaml
+ - name: custom
+ metadata: data/custom/metadata.tsv
+ sequences: data/custom/sequences.fasta
+
+ linelist:
+ geo: division
+ ```
+
+1. **How do I include more of my custom metadata columns into the linelists?**
+
+ - By default, the mandatory columns `strain`, `date`, and `country` will appear from your metadata.
+ - Extra columns can be supplied as a parameter to `summary` in your `builds.yaml` file.
+ - In the following example, the columns `division`, and `genbank_accession` will be extracted from your input `metadata.tsv` file and included in the final linelists.
+
+ ```yaml
+ - name: controls
+ metadata: data/controls/metadata.tsv
+ sequences: data/controls/sequences.fasta
+
+ summary:
+ extra_cols:
+ - genbank_accession
+ - division
+ ```
+
+1. **Where can I find the plotting data?**
+
+ - A data table is provided for each plot:
+
+ - Plot: `results/tutorial/plots/lineage.png`
+ - Table: `results/tutorial/plots/lineage.tsv`
+ - The rows are the epiweek, and the columns are the categories (ex. lineages)
+
+1. **Why are "positive" sequences missing from the plots and slides?**
+
+ - First check and see if they are in `plots_historical` and `report_historical` which summarize all sequences regardless of collection date.
+ - The most likely reason is that these sequences fall outside of the reporting period.
+ - The default reporting period is set to 16 weeks before the present.
+ - To change it for a build, add custom `plot` parameters to your `builds.yaml` file.
+
+ ```yaml
+ - name: custom
+ metadata: data/custom/metadata.tsv
+ sequences: data/custom/sequences.fasta
+
+ plot:
+ min_date: "2022-01-10"
+ max_date: "2022-04-25" # Optional, can be left blank to use current date
+ ```
diff --git a/docs/sphinx/source/high-performance-computing.md b/docs/sphinx/source/high-performance-computing.md
new file mode 100644
index 0000000..28d7692
--- /dev/null
+++ b/docs/sphinx/source/high-performance-computing.md
@@ -0,0 +1,81 @@
+## High Performance Computing
+
+`ncov-recombinant` can alternatively be dispatched using the SLURM job submission system.
+
+1. Create an HPC-compatible profile to store your build configuration.
+
+ ```bash
+ scripts/create_profile.sh --data data/custom --hpc
+ ```
+
+ ```text
+ 2022-06-17 09:16:55 Searching for metadata (data/custom/metadata.tsv)
+ 2022-06-17 09:16:55 SUCCESS: metadata found
+ 2022-06-17 09:16:55 Checking for 3 required metadata columns (strain date country)
+ 2022-06-17 09:16:55 SUCCESS: 3 columns found.
+ 2022-06-17 09:16:55 Searching for sequences (data/custom/sequences.fasta)
+ 2022-06-17 09:16:55 SUCCESS: Sequences found
+ 2022-06-17 09:16:55 Checking that the metadata strains match the sequence names
+ 2022-06-17 09:16:55 SUCCESS: Strain column matches sequence names
+ 2022-06-17 09:16:55 Creating new profile directory (my_profiles/custom-hpc)
+ 2022-06-17 09:16:55 Creating build file (my_profiles/custom-hpc/builds.yaml)
+ 2022-06-17 09:16:55 Adding default input data (defaults/inputs.yaml)
+ 2022-06-17 09:16:55 Adding custom input data (data/custom)
+ 2022-06-17 09:16:55 Adding `custom` as a build
+ 2022-06-17 09:16:55 Creating system configuration (my_profiles/custom-hpc/config.yaml)
+ 2022-06-17 09:16:55 Adding default HPC system resources
+ 2022-06-17 09:16:55 System resources can be further configured in:
+
+ my_profiles/custom-hpc/config.yaml
+
+ 2022-06-17 09:16:55 Builds can be configured in:
+
+ my_profiles/custom-hpc/builds.yaml
+
+ 2022-06-17 09:16:55 The custom-hpc profile is ready to be run with:
+
+ scripts/slurm.sh --profile my_profiles/custom-hpc
+ ```
+
+2. Edit `my_profiles/custom-hpc/config.yaml` to specify the number of `jobs` and `default-resources` to use.
+
+ ```yaml
+ # Maximum number of jobs to run simultaneously
+ jobs : 4
+
+ # Default resources for a SINGLE JOB
+ default-resources:
+ - cpus=64
+ - mem_mb=64000
+ - time_min=720
+ ```
+
+3. Dispatch the workflow using the slurm wrapper script:
+
+ ```bash
+ scripts/slurm.sh --profile my_profiles/custom-hpc
+ ```
+
+ > - **Tip**: Display log of most recent workflow: `cat $(ls -t logs/ncov-recombinant/*.log | head -n 1)`
+
+4. Use the `--help` parameter to get additional options for SLURM dispatch.
+
+ ```bash
+ scripts/slurm.sh --help
+ ```
+
+ ```text
+ usage: bash slurm.sh [-h] [--profile PROFILE] [--conda-env CONDA_ENV] [--target TARGET] [--cpus CPUS] [--mem MEM]
+
+ Dispatch a Snakemake pipeline using SLURM.
+
+ Required arguments:
+ --profile PROFILE Snakemake profile to execute (ex. profiles/tutorial-hpc)
+
+ Optional arguments:
+ --conda-env CONDA_ENV Conda environment to use. (default: ncov-recombinant)
+ --target TARGET Snakemake target(s) to execute (default: all)
+ --cpus CPUS CPUS to use for the main pipeline. (default: 1)
+ --mem MEM Memory to use for the ain pipeline. (default: 4GB)
+ -h, --help Show this help message and exit.
+ ```
diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst
new file mode 100644
index 0000000..d7eeb58
--- /dev/null
+++ b/docs/sphinx/source/index.rst
@@ -0,0 +1,32 @@
+.. ncov-recombinant documentation master file, created by
+ sphinx-quickstart on Tue Jan 24 16:59:57 2023.
+ You can adapt this file completely to your liking, but it should at least
+ contain the root `toctree` directive.
+
+ncov-recombinant
+============================================
+
+.. include:: description.rst
+
+.. toctree::
+ :maxdepth: 2
+ :caption: Table of Contents
+
+ what_is_a_recombinant.md
+ install.md
+ update.md
+ tutorial.md
+ output.md
+ controls.md
+ configuration.md
+ high-performance-computing.md
+ developers_guide.md
+ faq.md
+ credits.md
+
+
+**Indices and Tables**
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/docs/sphinx/source/install.md b/docs/sphinx/source/install.md
new file mode 100644
index 0000000..e212240
--- /dev/null
+++ b/docs/sphinx/source/install.md
@@ -0,0 +1,15 @@
+## Install
+
+1. Clone the repository:
+
+ ```bash
+ git clone https://github.com/ktmeaton/ncov-recombinant.git
+ cd ncov-recombinant
+ ```
+
+2. Install dependencies in a conda environment (`ncov-recombinant`):
+
+ ```bash
+ mamba env create -f workflow/envs/environment.yaml
+ conda activate ncov-recombinant
+ ```
diff --git a/docs/sphinx/source/output.md b/docs/sphinx/source/output.md
new file mode 100644
index 0000000..1a3869a
--- /dev/null
+++ b/docs/sphinx/source/output.md
@@ -0,0 +1,34 @@
+## Output
+
+### Tables
+
+Linelists are collated into a spreadsheet for excel/google sheets:
+
+1. `lineage`: The recombinant lineages observed.
+1. `parents`: The parental combinations observed.
+1. `linelist`: Results from all input sequences (minimal statistics).
+1. `summary`: Results from all input sequences (all possible statistics, for troubleshooting).
+1. `positives`: Results from sequences classified as a recombinant , as verified by breakpoint detection with [sc2rf](https://github.com/lenaschimmel/sc2rf).
+1. `false_positives`: Results from sequences flagged as recombinants by Nextclade, that were not verified by [sc2rf](https://github.com/lenaschimmel/sc2rf).
+1. `negatives`: Results from sequences classifed as a non-recombinant by nextclade.
+1. `issues`: Metadata of issues related to recombinant lineages posted in the [pango-designation](https://github.com/cov-lineages/pango-designation/issues) repository.
+
+
+
+### Slides
+
+Powerpoint/google slides with plots embedded for presenting.
+
+
+
+### Breakpoints
+
+Visualization of breakpoints by parental clade and parental lineage.
+
+| Clade | Lineage |
+|:--------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|
+|  |  |
+
+Visualization of parental alleles and mutations from [sc2rf](https://github.com/lenaschimmel/sc2rf).
+
+
diff --git a/docs/sphinx/source/tutorial.md b/docs/sphinx/source/tutorial.md
new file mode 100644
index 0000000..b5adc9e
--- /dev/null
+++ b/docs/sphinx/source/tutorial.md
@@ -0,0 +1,30 @@
+## Tutorial
+
+> **Tip**: Remember to run `conda activate ncov-recombinant` first!
+
+1. Preview the steps that are going to be run.
+
+ ```bash
+ snakemake --profile profiles/tutorial --dryrun
+ ```
+
+1. Run the workflow.
+
+ ```bash
+ snakemake --profile profiles/tutorial
+ ```
+
+1. Explore the Output .
+
+ - Slides | `results/tutorial/report/report.pptx`
+ - Tables* | `results/tutorial/report/report.xlsx`
+ - Plots
+ - Reporting Period (default: last 16 weeks): `results/tutorial/plots`
+ - All sequences: `results/tutorial/plots_historical`
+ - Breakpoints
+ - By parental clade: `results/tutorial/plots_historical/breakpoints_clade.png`
+ - By parental lineage: `results/tutorial/plots_historical/breakpoints_lineage.png`
+ - Alleles † | `results/tutorial/sc2rf/recombinants.ansi.txt`
+
+* Individual tables are available as TSV linelists in `results/tutorial/linelists`.
+† Visualize sc2rf mutations with `less -S` or [Visual Studio ANSI Colors](https://marketplace.visualstudio.com/items?itemName=iliazeus.vscode-ansi).
diff --git a/docs/sphinx/source/update.md b/docs/sphinx/source/update.md
new file mode 100644
index 0000000..6021bfa
--- /dev/null
+++ b/docs/sphinx/source/update.md
@@ -0,0 +1,27 @@
+## Update
+
+> **Tip**: It is recommended to do a fresh install in a separate directory to test a newer version.
+
+After pulling a fresh copy of the git repository, don't forget to update your conda environment!
+
+```bash
+mamba env update -f workflow/envs/environment.yaml
+```
+
+After running the newly installed version, you can compare lineage assignment changes using the following script:
+
+```bash
+python3 scripts/compare_positives.py \
+ --positives-1 old_pipeline_ver/results/controls/linelists/positives.tsv \
+ --positives-2 new_pipeline_ver/results/controls/linelists/positives.tsv \
+ --ver-1 "old_ver" \
+ --ver-2 "new_ver" \
+ --outdir compare/controls \
+ --node-order alphabetical
+```
+
+A comparative report is provided for each major or minor release:
+
+- `v0.6.1` → `v0.7.0` : [docs/testing_summary_package/ncov-recombinant_v0.6.1_v0.7.0.html](https://ktmeaton.github.io/ncov-recombinant/docs/testing_summary_package/ncov-recombinant_v0.6.1_v0.7.0.html)
+- `v0.5.1` → `v0.6.0` : [docs/testing_summary_package/ncov-recombinant_v0.5.1_v0.6.0.html](https://ktmeaton.github.io/ncov-recombinant/docs/testing_summary_package/ncov-recombinant_v0.5.1_v0.6.0.html)
+- `v0.4.2` → `v0.5.0` : [docs/testing_summary_package/ncov-recombinant_v0.4.2_v0.5.0.html](https://ktmeaton.github.io/ncov-recombinant/docs/testing_summary_package/ncov-recombinant_v0.4.2_v0.5.0.html)
diff --git a/docs/sphinx/source/what_is_a_recombinant.md b/docs/sphinx/source/what_is_a_recombinant.md
new file mode 100644
index 0000000..8ef2813
--- /dev/null
+++ b/docs/sphinx/source/what_is_a_recombinant.md
@@ -0,0 +1,30 @@
+## What is recombination?
+
+Recombination is an evolutionary mechanism where two distinct organisms exchange genetic code to produce a chimeric genome ([VanInsberghe et al. 2021](https://https://doi.org/10.1093/ve/veab059)). This manifests as a novel combination of alleles into blocks or "chunks" across the genome (Figure 1).
+
+Recombination is of particular concern for genomic surveillance, because it can lead to _substantial genetic change_ over a _short period of time_. Public health programs take into account this _rate of genetic change_ to inform policy recommendations, such as vaccine effectiveness and booster frequency ([Malik et al. 2022](https://doi.org/10.1016/j.jiph.2021.12.014)).
+
+SARS-CoV-2 has an estimated _substitution rate_ of 2 substitutions/month or 24 substitutions/year ([Tay et al. 2022](https://doi.org/10.1093/molbev/msac013)). However, the _recombination_ event that led to the Delta-Omicron recombinant `XD` produced a chimeric Delta genome with 25 new substitutions originating from Omicron (Figure 1). This single event produced the same amount of mutations as would have been expected over the course of a year. While more mutations are not necessarily beneficial to the organism, there remains the concerning possibility that recombination can rapidly increase the infectivity and severity of a pathogen.
+
+
+
+Figure 1. Genomic composition of the Delta-Omicron recombinant `XD`.
+
+### Pipeline Definition
+
+A recombinant SARS-CoV-2 lineage is defined as sequences with unique combinations of:
+
+1. Lineage assignment (ex. `XD`)
+1. Parental clades (ex. `Delta`/`Omicron`/`Delta`)
+1. Parental lineages (ex. `AY.4`/`BA.1`/`AY.4`)
+1. Breakpoint intervals (ex. Breakpoint 1: `21988-22672`, Breakpoint 2: `25470-25583`)
+
+If two sequences share the same lineage assignment (`XD`) and parental clades (ex. `Delta`/`Omicron`) but differ in their parental lineages (`AY.4`/`BA.1`/`AY.4` vs. `AY.44`/`BA.1`/`AY.44`), they will be classified as distinct recombinant lineages. The sequence with parental lineages `AY.44`/`BA.1`/`AY.444` will be renamed `XD-like`. This indicates that its closest known lineage is `XD` but that it differs from the expected genomic composition of true `XD`.
+
+### Designated Recombinants
+
+Designated recombinants from [pango-designation](https://github.com/cov-lineages/pango-designation) can be identified in the "positives" pipeline output by a lineage assignment that starts with `X` (ex. `XD`, `XBB`).
+
+### Novel Recombinants
+
+Novel recombinants (i.e. undesignated) can be identified in the "positives" pipeline output by a lineage assignment that _does not_ start with `X*` (ex. `BA.1.1`) _or_ with a lineage assignment that contains `-like` (ex. `XM-like`).
diff --git a/docs/testing_summary_package/ncov-recombinant_v0.6.1_v0.7.0.html b/docs/testing_summary_package/ncov-recombinant_v0.6.1_v0.7.0.html
new file mode 100644
index 0000000..5f9c3c1
--- /dev/null
+++ b/docs/testing_summary_package/ncov-recombinant_v0.6.1_v0.7.0.html
@@ -0,0 +1,3636 @@
+
+
+
+ ncov-recombinant v0.6.1 - v0.7.0
+
+
+
+
+Test Summary Package
+
+
+This report
+was automatically generated
+on February 28, 2023.
+
+
+Authors
+
+Katherine Eaton
+| National Microbiology Laboratory, PHAC
+| katherine.eaton@phac-aspc.gc.ca
+1. Summary
+The ncov-recombinant update from v0.6.1 to v0.7.0 has 3 major changes.
+The first change is a nextclade dataset upgrade from 2022-10-27 to 2023-02-01 which adds nomenclature for newly designated recombinants XBH to XBP.
+The second change is detection of recursive recombinants, XBL and XBN which arose from two separate recombination events between BA.2.75* and XBB*. Currently, recursive recombination is only set to be detected between XBB and VOC circulating in late 2022 and early 2023.
+The third major change is that all documentation has been migrated to Read The Docs . This includes a detailed Developer’s Guide for those looking to contribute to the project.
+Between v0.6.1 and v0.7.0, 15.2% of sequences in the controls-gisaid dataset had different detection results. 5.1% of sequences were newly classified (NA → X) and represent lineages not present in the v0.6.1 model. 6.6% of sequences had lineage assignment changes and 3.5% of sequences had sublineage assignment changes as a result of the Nextclade dataset upgrade. 0% of positive controls were dropped (X → NA), indicating no observed loss in sensitivity.
+ncov-recombinant v0.7.0 is a recommended upgrade for recombinant surveillance to accurately classify the latest recombinant lineages (up to XBP) and to detect recursive recombination (ex. XBL is a recombinant of XBB).
+For a comprehensive summary of the methodological changes, please see the release notes for v0.7.0
+2. Purpose
+Verify that the update of ncov-recombinant pipeline from version 0.6.1 to0.7.0:
+
+Maintains specificity for recombinants trained in previous versions.
+Increases sensitivity for newly designated recombinant sublineages.
+
+
+
+
+3. Datasets
+Controls
+This dataset includes SARS-CoV-2 genomes from GISAID that reflect the known diversity of recombinant sequences to date. These include 572 positive controls (recombinants), representing lineages XA - XBP and 186 negative controls (non-recombinants) selected from the Nextstrain Reference Phylogeny .
+In total, 758 control sequences were used as input and a strain list is available here .
+Canada VirusSeq
+This dataset includes publicly available SARS-CoV-2 genomes from the Canadian VirusSeq Data Portal . Sequences were downloaded on 2023-01-23 and include 441,234 genomes in total.
+4. Procedure
+The snakemake pipelines for v0.6.1 and v0.7.0 were run independently on the controls-gisaid and virusseq datasets. Please see the Procedure section of the Supplementary for detailed command-line instructions.
+5. Results
+Controls GISAID
+
+84.8% (485/572) of lineage assignments were unchanged (Figure 1 ).5.1% (29/572) of sequences were newly classified as recombinants.6.6% (38/572) of sequences changed lineage assignments (ex. XBB → XBN).3.5% (20/572) of sequences changed sublineage assignments (ex. XBB.1 → XBB.1.5 or XAY.1 → XAY.2).0.0% (0/572) of sequences were dropped as positives (NA).
+
+Note : Lineage assignments in v0.7.0 are identical to those in pango-designation and are the expected values.
+
+
+
+Figure 1: Comparison of lineage assignments in the controls-gisaid dataset between v0.6.1 and v0.7.0. Strain names represent the cluster_id of novel lineages, which represents the sequence with the earliest collection date.
+
Canada VirusSeq
+
+66.3% (862/1300) of lineage assignments were unchanged (Figure 2 ).7.8% (101/1300) of sequences were newly classified as recombinants.1.5% (20/1300) of sequences changed lineage assignments (ex. XBB → XBN).21.9% (285/1300) of sequences changed sublineage assignments (ex. XBB.1 → XBB.1.5 or XAY.1 → XAY.2).2.5% (32/1300) of sequences were dropped as positives (NA).
+
+Note : Lineage assignments in v0.7.0 are identical to those in pango-designation and are the expected values.
+
+
+
+Figure 2: Comparison of lineage assignments in the controls-gisaid dataset between v0.6.1 and v0.7.0. Strain names represent the cluster_id of novel lineages, which represents the sequence with the earliest collection date.
+
Changes
+New Detections
+New detections NA → X*) result from the following changes in v0.7.0:
+
+Nextclade dataset upgrades to include newly designated lineages: XBG, XBK, XBM.
+
+
+
+
+
+
+XBG
+NA
+BA.2.76*, BA.5.2*
+
+
+XBK
+NA
+BA.5.2*, CJ.1*
+
+
+XBM
+NA
+BA.2.76*, BF.3*
+
+
+
+Lineage Changes
+Lineage changes v0.7.0:
+
+Nextclade dataset upgrades to include newly designated lineages: XBH, XBJ, XBL, XBN, XBP.
+
+
+
+
+
+
+XBH
+BY.1
+BA.2.3*, BA.2.75*
+
+
+XBJ
+BA.2.3.20
+BA.2.3*, BA.5.2*
+
+
+XBL
+XBB.1-like
+BA.2.75*, XBB*
+
+
+XBN
+XBB-like
+BA.2.75*, XBB*
+
+
+XBP
+XBD-like
+BA.2.75*,* BA.5*
+
+
+
+Sublineage changes v0.7.0:
+
+Nextclade dataset upgrades to include new sublineages for: XAY and XBB.
+
+
+
+
+
+
+XAY.2
+XAY, XAY-like
+BA.2*, Delta (21J)
+
+
+XBB.1
+XBB.1.1
+BA.2.10*, BA.2.75*
+
+
+XBB.1.5
+XBB.1, XBB-like
+BA.2.10*, BA.2.75*
+
+
+
+Dropped Positives
+Dropped positives virusseq dataset, and include the unpublished cluster_id hCoV-19/Canada/ON-PHL-22-53186/2022 (N=19, 2022-12-09 to 2023-01-02). In v0.6.1 this was classified as a BA.5.2/BA.5.3 recombinant with breakpoints extremely close to the 5’ termini (Figure 3 ). The most likely reason this is dropped in v0.7.0 is because the 3 mutations attributed to BA.5.2 are no longer considered diagnostic based on the latest global mutation frequencies.
+
+
+Figure 3: Genomic composition of the dropped positive (hCoV-19/Canada/ON-PHL-22-53186/2022) which is composed of 19 sequences with identical mutation profiles.
+
Acknowledgements
+The results here are in whole, or in part based upon data hosted at the Canadian VirusSeq Data Portal: https://virusseq-dataportal.ca/ . We wish to acknowledge the Canadian Public Health Laboratory Network (CPHLN), Genome Canada and the CanCOGeN VirusSeq Consortium for their contribution to the Portal.
+Supplementary
+Procedure
+Download Data
+
+Download the GISAID sequences and metadata in the strains list from GISAID to data/controls-gisaid/.
Download the VirusSeq sequences and metadata.
+wget -O virusseq.tar.gz https://singularity.virusseq-dataportal.ca/download/archive/2d9ace2c-0808-475f-bc93-6ad5808581a4tar -xvf virusseq.tar.gzmkdir data/virusseq# Prep metadata csvtk cut -t -f "fasta header name,sample collection date,geo_loc_name (country),geo_loc_name (state/province/territory)" * files-archive* .tsv \ | csvtk rename -t -f "fasta header name" -n "strain" \ | csvtk rename -t -f "sample collection date" -n "date" \ | csvtk rename -t -f "geo_loc_name (country)" -n "country" \ | csvtk rename -t -f "geo_loc_name (state/province/territory)" -n "division" \ > data/virusseq/metadata.tsv# Prep sequences mv * files-archive* .fasta data/virusseq/sequences.fasta# Cleanup rm * files-archive* .tsvrm virusseq.tar.gz
+
+
+Download the pipeline.
+git clone https://github.com/ktmeaton/ncov-recombinant.git 0.7.0cd 0.7.0git checkout v0.7.0Create a version-controlled conda environment.
+# Local mamba env create -f workflow/envs/environment.yaml -n ncov-recombinant-0.7.0# HPC sbatch -J conda-ncov-recombinant-0.7.0 --wrap = "mamba env create -f workflow/envs/environment.yaml -n ncov-recombinant-0.7.0" Symlink the controls-gisaid data.
+ln -s ../../../data/controls-gisaid/metadata.tsv data/controls-gisaid/metadata.tsvln -s ../../../data/controls-gisaid/sequences.fasta data/controls-gisaid/sequences.fastaSymlink the virusseq data.
+ln -s ../../data/virusseq data/virusseqRun the pipeline for controls-gisaid.
+# Local conda activate ncov-recombinant-0.7.0snakemake --profile profiles/controls-gisaid# HPC scripts/slurm.sh --profile profiles/controls-gisaid-hpc --conda-env ncov-recombinant-0.7.0Run the pipeline for virusseq (must be done as HPC).
+scripts/slurm.sh --profile profiles/virusseq-hpc --conda-env ncov-recombinant-0.7.0
+Note: The pipeline will likely fail to run *_historical rules (ex. plot_historical, report_historical). This is because new bug fixes were introduced in v0.7.0 to catch errors relating to plotting extremely large datasets. This is tolerable, as for the test summary package, only the linelists are used for reporting here.
+
+
+
+Download the pipeline.
+git clone https://github.com/ktmeaton/ncov-recombinant.git 0.6.1cd 0.6.1git checkout v0.6.1-hotfix.1Create a version-controlled conda environment.
+# Local mamba env create -f workflow/envs/environment.yaml -n ncov-recombinant-0.6.1# HPC sbatch -J conda-ncov-recombinant-0.6.1 --wrap = "mamba env create -f workflow/envs/environment.yaml -n ncov-recombinant-0.6.1" Symlink the controls-gisaid data.
+ln -s ../../../data/controls-gisaid/metadata.tsv data/controls-gisaid/metadata.tsvln -s ../../../data/controls-gisaid/sequences.fasta data/controls-gisaid/sequences.fastaSymlink the virusseq data.
+ln -s ../../data/virusseq data/virusseqRun the pipeline for controls-gisaid.
+# Local conda activate ncov-recombinant-0.6.1snakemake --profile profiles/controls-gisaid# HPC scripts/slurm.sh --profile profiles/controls-gisaid-hpc --conda-env ncov-recombinant-0.6.1Run the pipeline for virusseq (must be done as HPC).
+scripts/slurm.sh --profile profiles/virusseq-hpc --conda-env ncov-recombinant-0.6.1
+Comparison
+After the pipelines are complete for each version, run the following to compare lineage assignments.
+old_ver = "0.6.1" new_ver = "0.7.0" Controls GISAID
+conda activate ncov-recombinant-0.7.0link_sizes = ( "1" "3" "5" "10" ) for size in ${link_sizes [@] } ; do python3 0.7.0/scripts/compare_positives.py \ --positives-1 ${old_ver} /results/controls-gisaid/linelists/positives.tsv \ --positives-2 ${new_ver} /results/controls-gisaid/linelists/positives.tsv \ --ver-1 "v ${old_ver} " \ --ver-2 "v ${new_ver} " \ --outdir compare/controls-gisaid-${size} \ --node-order alphabetical \ --min-link-size $size done Canada VirusSeq
+conda activate ncov-recombinant-0.7.0link_sizes = ( "1" "3" "5" "10" ) for size in ${link_sizes [@] } ; do python3 0.7.0/scripts/compare_positives.py \ --positives-1 ${old_ver} /results/virusseq/linelists/positives.tsv \ --positives-2 ${new_ver} /results/virusseq/linelists/positives.tsv \ --ver-1 "v ${old_ver} " \ --ver-2 "v ${new_ver} " \ --outdir compare/virusseq-${size} \ --node-order alphabetical \ --min-link-size $size done New Lineages
+old_ver = "0.6.1" new_ver = "0.7.0" csvtk cut -t -f "strain" ${old_ver} /results/controls-gisaid/linelists/positives.tsv \ | tail -n+2 \ | csvtk grep -t -f "strain" -P - -v ${new_ver} /results/controls-gisaid/linelists/positives.tsv \ | csvtk cut -t -f "strain" \ | tail -n+2 \ | csvtk grep -t -f "strain" -P - ${old_ver} /results/controls-gisaid/linelists/linelist.tsv \ | csvtk pretty -t \ | less -S Dropped Lineages
+csvtk cut -t -f "strain" ${new_ver} /results/controls-gisaid/linelists/positives.tsv \ | tail -n+2 \ | csvtk grep -t -f "strain" -P - -v ${old_ver} /results/controls-gisaid/linelists/positives.tsv \ | csvtk cut -t -f "strain" \ | tail -n+2 \ | csvtk grep -t -f "strain" -P - ${new_ver} /results/controls-gisaid/linelists/linelist.tsv \ | csvtk pretty -t \ | less -S
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diff --git a/images/breakpoints_XD_v0.7.0.png b/images/breakpoints_XD_v0.7.0.png
new file mode 100644
index 0000000..748fd0a
Binary files /dev/null and b/images/breakpoints_XD_v0.7.0.png differ
diff --git a/images/breakpoints_clade_v0.7.0.png b/images/breakpoints_clade_v0.7.0.png
new file mode 100644
index 0000000..ec8d996
Binary files /dev/null and b/images/breakpoints_clade_v0.7.0.png differ
diff --git a/images/breakpoints_lineage_v0.7.0.png b/images/breakpoints_lineage_v0.7.0.png
new file mode 100644
index 0000000..e8f14d2
Binary files /dev/null and b/images/breakpoints_lineage_v0.7.0.png differ
diff --git a/profiles/controls-gisaid/builds.yaml b/profiles/controls-gisaid/builds.yaml
index c68051d..b7464c7 100644
--- a/profiles/controls-gisaid/builds.yaml
+++ b/profiles/controls-gisaid/builds.yaml
@@ -22,3 +22,9 @@ builds:
linelist:
min_private_muts: 999
+
+ plot:
+ min_cluster_size: 2
+
+ plot_historical:
+ min_cluster_size: 2
diff --git a/profiles/virusseq-hpc/builds.yaml b/profiles/virusseq-hpc/builds.yaml
new file mode 100644
index 0000000..01a58c9
--- /dev/null
+++ b/profiles/virusseq-hpc/builds.yaml
@@ -0,0 +1,38 @@
+# -----------------------------------------------------------------------------
+# Builds
+# -----------------------------------------------------------------------------
+
+builds:
+
+ # ---------------------------------------------------------------------------
+ # `controls-gisaid` uses a mixture of positive and negative recombinants from GISAID
+
+ - name: controls-gisaid
+ metadata: data/controls-gisaid/metadata.tsv
+ sequences: data/controls-gisaid/sequences.fasta
+
+ linelist:
+ min_private_muts: 999
+
+ # ---------------------------------------------------------------------------
+ # `virusseq` scans all public SARS-CoV-2 genomes from Canada
+
+ - name: virusseq
+ metadata: data/virusseq/metadata.tsv
+ sequences: data/virusseq/sequences.fasta
+
+ sc2rf:
+ exclude_negatives: true
+
+ summary:
+ extra_cols:
+ - division
+
+ linelist:
+ geo: "division"
+
+ plot:
+ min_cluster_size: 2
+
+ plot_historical:
+ min_cluster_size: 2
diff --git a/profiles/virusseq-hpc/config.yaml b/profiles/virusseq-hpc/config.yaml
new file mode 100644
index 0000000..412185e
--- /dev/null
+++ b/profiles/virusseq-hpc/config.yaml
@@ -0,0 +1,43 @@
+#------------------------------------------------------------------------------#
+# Build Config
+#------------------------------------------------------------------------------#
+configfile:
+ - defaults/parameters.yaml
+ - profiles/virusseq-hpc/builds.yaml
+
+#------------------------------------------------------------------------------#
+# Snakemake config
+#------------------------------------------------------------------------------#
+restart-times: 1
+max-jobs-per-second: 1
+latency-wait: 60
+keep-going: True
+show-failed-logs: True
+rerun-incomplete: True
+printshellcmds: False
+
+#------------------------------------------------------------------------------#
+# System config
+#------------------------------------------------------------------------------#
+
+# Maximum number of jobs to run
+jobs : 20
+
+# Default resources for a SINGLE JOB
+# Divide your total resources by the maximum number of jobs
+default-resources:
+ - cpus=16
+ - mem_mb=16000
+ - time_min=720
+
+# What commands should be run to DISPATCH a job?
+cluster:
+ mkdir -p logs/slurm/{rule} &&
+ sbatch
+ --cpus-per-task={resources.cpus}
+ --mem={resources.mem_mb}
+ --job-name=ncov-recombinant:{rule}:{wildcards}
+ --output=logs/slurm/{rule}/{wildcards}-%j.out
+
+# What commands should be run to CANCEL a job?
+cluster-cancel: scancel
diff --git a/resources/breakpoints.tsv b/resources/breakpoints.tsv
index 561f13a..91436d9 100644
--- a/resources/breakpoints.tsv
+++ b/resources/breakpoints.tsv
@@ -67,16 +67,28 @@ XAS 882 23041:26528 Omicron/BA.5/22B,Omicron/BA.2/21L
XAT 885 26062:26528 Omicron/BA.2/21L,Omicron/BA.1/21K
XAU 894 2833:4183 Omicron/BA.1/21K,Omicron/BA.2/21L
XAW 895 20056:22577,27875:28310 Delta/21J,Omicron/BA.2/21L,Delta/21J
-proposed896 896 22601:22915 Omicron/BA.2/21L,Omicron/BA.5/22B
+XBG 896 22600:22916 Omicron/BA.2.76,Omicron/BA.5.2
XAV 911 12161:22791 Omicron/BA.2/21L,Omicron/BA.5/22B
proposed1006 1006 26532:27381,27810:29665 Omicron/BA.5/22B,Omicron/BA.2/21L,Omicron/BA.5/22B
-XBB 1058 22332:22576 BA.2.10,Omicron/BA.2.75/22D
+XBB 1058 22332:22576 Omicron/BA.2.10,Omicron/BA.2.75/22D
+XBB_ARTICv4.1 1058 22191:22576 Omicron/BA.2.10,Omicron/BA.2.75/22D
XBC 1100 2791:4183,22579:22673,25470:25583 Omicron/BA.2/21L,Delta/21I,Omicron/BA.2/21L,Delta/21I
+XBD 1137 23020:23030 Omicron/BA.2.75/22D,Omicron/BA.5/22B
proposed1138 1138 NA NA
proposed1139 1139 12161:22791 Omicron/BA.2/21L,Omicron/BA.5/22B
-XBD 1137 23020:25415 Omicron/BA.2.75/22D,Omicron/BA.5/22B
-proposed1229 1229 15452:22000 BA.2.3.17,Omicron/BA.2.75/22D
-XBE 1246 16936:27011 Omicron/BA.5.2,Omicron/BA.5.3
+XBH 1229 20236:22000 Omicron/BA.2.3.17,Omicron/BA.2.75/22D
+XBE 1246 22943:27011 Omicron/BA.5.2,Omicron/BA.5.3
XBF 1259 5184:9865 Omicron/BA.5/22B,Omicron/BA.2.75/22D
-proposed1296 1296 5184:12443 Omicron/BA.2.75/22D,Omicron/XBB/22F
-proposed1268 1268 23019:23039 Omicron/BA.2/21L,Omicron/BA.5/22B
+XBJ 1268 23019:23039 Omicron/BA.2/21L,Omicron/BA.5/22B
+XBN 1296 5184:12443 Omicron/BA.2.75/22D,Omicron/XBB/22F
+proposed1305 1305 27013:27037 Omicron/BA.2.75/22D,Omicron/BA.5.2
+proposed1340 1340 20236:21809 Omicron/BA.2.75/22D,Omicron/XBB/22F
+XBP 1393 22191:22330 Omicron/BA.2.75/22D,Omicron/BA.5/22B
+XBK 1418 1628:3795 Omicron/BA.5.2,Omicron/CJ.1
+proposed1425 1425 22578:22892,25417:26274 Omicron/CJ.1,Omicron/BA.5.3,Omicron/CJ.1
+proposed1440 1440 1628:3795 Omicron/BA.5.2,Omicron/CJ.1
+XBM 1441 22600:22916 Omicron/BA.2.76,Omicron/BA.5.2
+XBL 1532 5184:15737 Omicron/CJ.1,Omicron/XBB/22F
+proposed1576 1576 5184:9865,22600:22628 Omicron/BA.5/22B,Omicron/BA.2.75/22D,Omicron/BA.5/22B
+proposed1444 1444 22600:22628 Omicron/BA.2.75/22D,Omicron/BA.5/22B
+XAY_dropout 844 6403:8985,9867:10197,12881:15450,20056:22199,24470:24999,27385:27637 Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L
diff --git a/resources/breakpoints_clade.png b/resources/breakpoints_clade.png
index 2efa784..80dc4b6 100644
Binary files a/resources/breakpoints_clade.png and b/resources/breakpoints_clade.png differ
diff --git a/resources/breakpoints_clade.svg b/resources/breakpoints_clade.svg
index 1d798c1..f63f0ad 100644
--- a/resources/breakpoints_clade.svg
+++ b/resources/breakpoints_clade.svg
@@ -1,16 +1,16 @@
-
+
2022-11-08T12:15:34.872105
+ 2023-02-22T11:46:32.784405
image/svg+xml
- Matplotlib v3.5.2, https://matplotlib.org/
+ Matplotlib v3.6.3, https://matplotlib.org/
@@ -21,8 +21,8 @@
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+ 5000
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+ 20000
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+ 25000
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+ 30000
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+ Genomic Coordinate
-
-
- XA
+ XA
-
- XC
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-
- proposed808-1
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-
- proposed808-2
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-
- XB
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-
- XBB
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-
- proposed1229
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-
- XBC
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-
- XF
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-
- proposed446
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-
- XS
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-
- proposed519
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-
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-
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-
- XD
+ XAY
-
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+ XAY_dropout
-
- XAY
+ XAU
-
- XAU
+ XN
-
- XN
+ XW
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+ XAR
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+ XQ
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+ XR
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+ XG
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+ XL
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+ XU
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+ XAA
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+ XAB
-
- XAB
+ XAG
-
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+ XAM
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- XAM
+ proposed470
-
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- proposed479
+ XH
-
- XH
+ XE
-
- XE
+ XAF
-
- XAF
+ proposed518
-
- proposed518
+ proposed557
-
- proposed557
+ XY
-
- XY
+ XJ
-
- XJ
+ XV
-
- XV
+ proposed467
-
- proposed467
+ proposed559
-
- proposed559
+ XK
-
- XK
+ XM
-
- XM
+ proposed781
-
- proposed781
+ XAQ
-
- XAQ
+ XAL
-
- XAL
+ proposed514
-
- proposed514
+ proposed477
-
- proposed477
+ XP
-
- XP
+ proposed513
-
- proposed513
+ XAK
-
- XAK
+ proposed498
-
- proposed498
+ XAC
-
- XAC
+ proposed608
-
- proposed608
+ XAE
-
- XAE
+ XT
-
- XT
+ proposed482
-
- proposed482
+ XAD
-
- XAD
+ XAT
-
- XAT
+ XZ
-
- XZ
+ XAH
-
- XAH
+ XAP
-
- XAP
+ XBB_ARTICv4.1
-
- XAJ
+ XBB
-
- proposed1296
+ XAJ
-
- XBD
+ XBH
-
- XBF
+ XBN
-
- XAZ
+ proposed1340
-
- XAN
+ XBP
-
- XAV
+ proposed1444
-
- proposed1139
+ XBD
-
- proposed896
+ proposed1305
-
- proposed1268
+ proposed1576
-
- XAS
+ XBF
-
- proposed1006
+ XBG
-
- XBE
+ XBM
-
- proposed820
+ XAZ
-
- proposed1138
+ XAN
+
+
+
+
+
+
+
+
+ XAV
-
- Lineage
+
+
+
+
+
+
+ proposed1139
+
+
+
+
+
+
+
+
+ XBJ
+
+
+
+
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+ XAS
+
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+
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+
+
+ proposed1006
+
+
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+ proposed1440
+
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+ XBE
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+ proposed1425
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+
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+
+
+
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+
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+
+
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+
+
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+
-
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+
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+
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-
-
+
-
- B.1.177/20E
+
+ B.1.177/20E
-
-
+
-
- Alpha/B.1.1.7/20I
+
+ Alpha/B.1.1.7/20I
-
-
+
-
- B.1.634
+
+ B.1.634
+
+
+
-
-
+ B.1.631
+
+
+
-
- B.1.631
+
+ Delta/21J
-
-
+
-
- Delta/21J
+
+ Omicron/BA.1/21K
-
-
+
-
- Omicron/BA.1/21K
+
+ Omicron/BA.2/21L
+
+
+
-
-
+ Omicron/BA.5/22B
+
+
+
-
- Omicron/BA.2/21L
+
+ B.1.438.1
-
-
+
-
- Omicron/BA.5/22B
+
+ Omicron/BA.2.12/22C
-
-
+
-
- B.1.438.1
+
+ Omicron/BA.4/22A
-
-
+
-
- Omicron/BA.2.12/22C
+
+ Omicron/BA.2.76
-
-
+
-
- Omicron/BA.4/22A
+
+ Omicron/BA.5.2
+
+
+
+
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-
-
+
-
- BA.2.10
+
+ Omicron/BA.2.75/22D
-
-
+
-
- Omicron/BA.2.75/22D
+
+ Delta/21I
-
-
+
-
- Delta/21I
+
+ Omicron/BA.2.3.17
-
-
+
-
- BA.2.3.17
+
+ Omicron/BA.5.3
-
-
+
-
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+
+ Omicron/XBB/22F
-
-
-
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-
-
-
+
-
- Omicron/XBB/22F
+
+ Omicron/CJ.1
-
-
+
-
- Unknown
+
+ Unknown
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-
+
-
- Breakpoint
+
+ Breakpoint
-
+
-
-
-
- Substitution
+
+ Substitution
-
-
+
diff --git a/resources/breakpoints_clade.tsv b/resources/breakpoints_clade.tsv
index 3982ba4..a6b482d 100644
--- a/resources/breakpoints_clade.tsv
+++ b/resources/breakpoints_clade.tsv
@@ -7,8 +7,6 @@ proposed808-2 B.1.438.1 0 18163
proposed808-1 B.1.438.1 18163 24503
XB B.1.631 23012 29903
XB B.1.634 0 22916
-XBB BA.2.10 0 22331
-proposed1229 BA.2.3.17 0 15451
XBC Delta/21I 4184 22578
XBC Delta/21I 25584 29903
XF Delta/21J 0 5386
@@ -24,8 +22,11 @@ XD Delta/21J 0 21987
XC Delta/21J 0 26767
XBA Delta/21J 4181 21846
XAY Delta/21J 8986 9866
+XAY_dropout Delta/21J 8986 9866
+XAY_dropout Delta/21J 15451 20055
XAY Delta/21J 15451 21846
XAY Delta/21J 25000 27384
+XAY_dropout Delta/21J 25000 27384
XBA Delta/21J 25000 29903
XD Delta/21J 25584 29903
XAW Delta/21J 28311 29903
@@ -85,25 +86,36 @@ XAT Omicron/BA.1/21K 26529 29903
XZ Omicron/BA.1/21K 26530 29903
XAH Omicron/BA.1/21K 26530 29903
XAP Omicron/BA.1/21K 26530 29903
+XBB_ARTICv4.1 Omicron/BA.2.10 0 22190
+XBB Omicron/BA.2.10 0 22331
XAJ Omicron/BA.2.12/22C 0 21570
XAJ Omicron/BA.2.12/22C 23673 29903
-proposed1296 Omicron/BA.2.75/22D 0 5183
+XBH Omicron/BA.2.3.17 0 20235
+XBN Omicron/BA.2.75/22D 0 5183
+proposed1340 Omicron/BA.2.75/22D 0 20235
+XBP Omicron/BA.2.75/22D 0 22190
+proposed1444 Omicron/BA.2.75/22D 0 22599
XBD Omicron/BA.2.75/22D 0 23019
+proposed1305 Omicron/BA.2.75/22D 0 27012
+proposed1576 Omicron/BA.2.75/22D 9866 22599
XBF Omicron/BA.2.75/22D 9866 29903
-proposed1229 Omicron/BA.2.75/22D 22001 29903
+XBH Omicron/BA.2.75/22D 22001 29903
XBB Omicron/BA.2.75/22D 22577 29903
+XBB_ARTICv4.1 Omicron/BA.2.75/22D 22577 29903
+XBG Omicron/BA.2.76 0 22599
+XBM Omicron/BA.2.76 0 22599
XBA Omicron/BA.2/21L 0 2790
XBC Omicron/BA.2/21L 0 2790
XAZ Omicron/BA.2/21L 0 3358
XAY Omicron/BA.2/21L 0 6402
+XAY_dropout Omicron/BA.2/21L 0 6402
proposed513 Omicron/BA.2/21L 0 11537
XAN Omicron/BA.2/21L 0 12160
XAV Omicron/BA.2/21L 0 12160
proposed1139 Omicron/BA.2/21L 0 12160
XAK Omicron/BA.2/21L 0 13194
proposed498 Omicron/BA.2/21L 0 17409
-proposed896 Omicron/BA.2/21L 0 22600
-proposed1268 Omicron/BA.2/21L 0 23018
+XBJ Omicron/BA.2/21L 0 23018
XAC Omicron/BA.2/21L 0 24503
proposed608 Omicron/BA.2/21L 0 24503
XAE Omicron/BA.2/21L 0 24503
@@ -130,6 +142,7 @@ XAM Omicron/BA.2/21L 8393 29903
proposed470 Omicron/BA.2/21L 9344 29903
proposed479 Omicron/BA.2/21L 10029 29903
XAY Omicron/BA.2/21L 10198 12880
+XAY_dropout Omicron/BA.2/21L 10198 12880
XH Omicron/BA.2/21L 11537 29903
XE Omicron/BA.2/21L 11537 29903
XAF Omicron/BA.2/21L 11537 29903
@@ -152,30 +165,50 @@ proposed484 Omicron/BA.2/21L 21762 29903
XAK Omicron/BA.2/21L 21763 29903
XAY Omicron/BA.2/21L 22200 24469
XBA Omicron/BA.2/21L 22200 24469
+XAY_dropout Omicron/BA.2/21L 22200 24469
XAW Omicron/BA.2/21L 22578 27874
XBC Omicron/BA.2/21L 22674 25469
proposed498 Omicron/BA.2/21L 24504 29903
XAS Omicron/BA.2/21L 26529 29903
proposed1006 Omicron/BA.2/21L 27382 27809
XAY Omicron/BA.2/21L 27638 29903
+XAY_dropout Omicron/BA.2/21L 27638 29903
XP Omicron/BA.2/21L 29510 29903
XAJ Omicron/BA.4/22A 23018 23535
-XBE Omicron/BA.5.2 0 16935
+XBK Omicron/BA.5.2 0 1627
+proposed1440 Omicron/BA.5.2 0 1627
+XBE Omicron/BA.5.2 0 22942
+XBG Omicron/BA.5.2 22917 29903
+XBM Omicron/BA.5.2 22917 29903
+proposed1305 Omicron/BA.5.2 27038 29903
+proposed1425 Omicron/BA.5.3 22893 25416
XBE Omicron/BA.5.3 27012 29903
XBF Omicron/BA.5/22B 0 5183
+proposed1576 Omicron/BA.5/22B 0 5183
XAS Omicron/BA.5/22B 0 23040
proposed1006 Omicron/BA.5/22B 0 26531
proposed820 Omicron/BA.5/22B 0 29903
XAZ Omicron/BA.5/22B 9866 29903
+XBP Omicron/BA.5/22B 22331 29903
+proposed1576 Omicron/BA.5/22B 22629 29903
+proposed1444 Omicron/BA.5/22B 22629 29903
XAN Omicron/BA.5/22B 22792 29903
XAV Omicron/BA.5/22B 22792 29903
proposed1139 Omicron/BA.5/22B 22792 29903
-proposed896 Omicron/BA.5/22B 22916 29903
-proposed1268 Omicron/BA.5/22B 23040 29903
-XBD Omicron/BA.5/22B 25416 29903
+XBD Omicron/BA.5/22B 23031 29903
+XBJ Omicron/BA.5/22B 23040 29903
proposed1006 Omicron/BA.5/22B 29666 29903
-proposed1296 Omicron/XBB/22F 12444 29903
+XBL Omicron/CJ.1 0 5183
+proposed1425 Omicron/CJ.1 0 22577
+XBK Omicron/CJ.1 3796 29903
+proposed1440 Omicron/CJ.1 3796 29903
+proposed1425 Omicron/CJ.1 26275 29903
+XBN Omicron/XBB/22F 12444 29903
+XBL Omicron/XBB/22F 15738 29903
+proposed1340 Omicron/XBB/22F 21810 29903
proposed1138 Unknown 0 29903
+XBK breakpoint 1628 3795
+proposed1440 breakpoint 1628 3795
XBA breakpoint 2791 4180
XBC breakpoint 2791 4183
XAU breakpoint 2833 4183
@@ -186,7 +219,9 @@ XAZ breakpoint 3359 9865
XQ breakpoint 4322 5385
XR breakpoint 4322 5385
XBF breakpoint 5184 9865
-proposed1296 breakpoint 5184 12443
+proposed1576 breakpoint 5184 9865
+XBN breakpoint 5184 12443
+XBL breakpoint 5184 15737
XF breakpoint 5387 6401
XG breakpoint 5387 8382
XL breakpoint 5387 8392
@@ -197,10 +232,12 @@ XAG breakpoint 5387 8392
XAM breakpoint 5387 8392
proposed446 breakpoint 5585 6511
XAY breakpoint 6403 8985
+XAY_dropout breakpoint 6403 8985
XS breakpoint 9054 10448
proposed470 breakpoint 9209 9343
proposed479 breakpoint 9534 10028
XAY breakpoint 9867 10197
+XAY_dropout breakpoint 9867 10197
proposed808-1 breakpoint 10030 10448
XH breakpoint 10448 11536
XE breakpoint 10448 11536
@@ -214,14 +251,13 @@ XAN breakpoint 12161 22791
XAV breakpoint 12161 22791
proposed1139 breakpoint 12161 22791
XAY breakpoint 12881 15450
+XAY_dropout breakpoint 12881 15450
XAK breakpoint 13195 15240
XJ breakpoint 13196 15239
XV breakpoint 13196 15239
proposed467 breakpoint 15241 15713
proposed808-1 breakpoint 15241 18162
-proposed1229 breakpoint 15452 22000
proposed559 breakpoint 15714 17410
-XBE breakpoint 16936 27011
proposed498 breakpoint 17410 18163
XK breakpoint 17411 19954
XM breakpoint 17411 19954
@@ -232,7 +268,10 @@ proposed808-2 breakpoint 18164 18876
proposed476 breakpoint 19221 21845
proposed514 breakpoint 20055 21618
proposed477 breakpoint 20056 21617
+XAY_dropout breakpoint 20056 22199
XAW breakpoint 20056 22577
+proposed1340 breakpoint 20236 21809
+XBH breakpoint 20236 22000
XA breakpoint 21256 21613
XAJ breakpoint 21571 23017
XAK breakpoint 21618 21762
@@ -242,21 +281,30 @@ XAY breakpoint 21847 22199
XBA breakpoint 21847 22199
proposed439 breakpoint 21988 22577
XD breakpoint 21988 22672
+XBP breakpoint 22191 22330
+XBB_ARTICv4.1 breakpoint 22191 22576
XBB breakpoint 22332 22576
+proposed1425 breakpoint 22578 22892
XBC breakpoint 22579 22673
-proposed896 breakpoint 22601 22915
+proposed1576 breakpoint 22600 22628
+proposed1444 breakpoint 22600 22628
+XBG breakpoint 22600 22916
+XBM breakpoint 22600 22916
XB breakpoint 22917 23011
-proposed1268 breakpoint 23019 23039
-XBD breakpoint 23020 25415
+XBE breakpoint 22943 27011
+XBJ breakpoint 23019 23039
+XBD breakpoint 23020 23030
XAS breakpoint 23041 26528
XAJ breakpoint 23536 23672
proposed498 breakpoint 24130 24503
XAY breakpoint 24470 24999
XBA breakpoint 24470 24999
+XAY_dropout breakpoint 24470 24999
proposed808-1 breakpoint 24504 25562
XAC breakpoint 24504 26059
proposed608 breakpoint 24504 26059
XAE breakpoint 24504 26059
+proposed1425 breakpoint 25417 26274
XD breakpoint 25470 25583
XBC breakpoint 25470 25583
XZ breakpoint 26061 26529
@@ -268,7 +316,9 @@ XAT breakpoint 26062 26528
XAD breakpoint 26063 26528
proposed1006 breakpoint 26532 27381
XC breakpoint 26768 27637
+proposed1305 breakpoint 27013 27037
XAY breakpoint 27385 27637
+XAY_dropout breakpoint 27385 27637
XP breakpoint 27385 29509
proposed1006 breakpoint 27810 29665
XAW breakpoint 27875 28310
diff --git a/resources/issue_to_lineage.tsv b/resources/issue_to_lineage.tsv
index de98856..5913243 100644
--- a/resources/issue_to_lineage.tsv
+++ b/resources/issue_to_lineage.tsv
@@ -1,9 +1,27 @@
+1637 XBR
+1603 XBB.2.3 XBB.2.4
+1576 XBT
+1567 XBS
+1551 XBK.1
+1532 XBL
+1487 XBB.1.13
+1455 XBB.1.8
+1441 XBM
+1440 XBQ
+1428 XBB.1.6
+1418 XBK
+1393 XBP
+1352 XBB.1.4.1
+1296 XBN
+1268 XBJ
1259 XBF
1246 XBE
+1229 XBH
1130 XBC.1
1100 XBC
1088 XBB.1
911 XAV
+896 XBG
895 XAW
894 XAU
885 XAT
@@ -48,6 +66,14 @@
263 XC
189 XB
63 XA
+1674 proposed1674
+1668 proposed1668
+1667 proposed1667
+1666 proposed1666
+1647 proposed1647
+1589 proposed1589
+1332 proposed1332
+1305 proposed1305
1252 proposed1252
1217 proposed1217
1006 proposed1006
@@ -55,7 +81,22 @@
781 proposed781
608 proposed608
514 proposed514
-1296 proposed1296
+1669 proposed1669
+1653 proposed1653
+1644 proposed1644
+1642 proposed1642
+1641 proposed1641
+1614 XBB.1.5.10
+1543 proposed1543
+1535 XBB.2.3
+1505 proposed1505
+1490 XBB.1.11 XBB.1.11.1
+1460 proposed1460
+1444 proposed1444
+1425 proposed1425
+1362 proposed1362
+1360 proposed1360
+1340 proposed1340
1255 XAY.1.1
1218 proposed1218
1208 XAY.1
@@ -63,7 +104,6 @@
1175 XBB.4
1139 proposed1139
1138 proposed1138
-896 proposed896
484 proposed484
701 proposed701
557 proposed557
@@ -81,10 +121,19 @@
439 proposed439
422 proposed422
210 proposed210
+1651 proposed1651
+1622 proposed1622
+1602 proposed1602
+1562 proposed1562
+1553 proposed1553
+1545 proposed1545
+1538 proposed1538
+1459 XBB.6.1
+1437 proposed1437
+1367 proposed1367
+1330 proposed1330
1273 proposed1273
-1268 proposed1268
1260 proposed1260
-1229 proposed1229
1222 proposed1222
1201 proposed1201
1188 proposed1188
@@ -104,3 +153,7 @@
808 proposed808-2
844 XAY
844 XBA
+1058 XBB_ARTICv4.1
+1440 proposed1440
+1576 proposed1576
+844 XAY_dropout
diff --git a/resources/issues.tsv b/resources/issues.tsv
index 0e141e9..859fff3 100644
--- a/resources/issues.tsv
+++ b/resources/issues.tsv
@@ -1,11 +1,29 @@
issue lineage status status_description countries parents_curated breakpoints_curated breakpoints title date_created date_updated date_closed
-1259 XBF designated Omicron/BA.5/22B,Omicron/BA.2.75/22D 5184:9865 The breakpoint seems to be before 9866. The event seems to have happened very recently, maybe 1-2 months ago as there are no private mutations in the 20k bp downstream of 9866. BA.5.2.3 x CJ.1 (BM.1.1.1.1) recombinant [AUS-VIC, 9 sequences as of 2022-10-29] 2022-10-29T00:47:58Z 2022-11-05T18:49:16Z 2022-11-03T21:55:05Z
-1246 XBE designated Omicron/BA.5.2,Omicron/BA.5.3 16936:27011 Putative recombinant between BA.5.2+Orf1b:1050N+S:346T and BE.4+29868A spotted by @Alurqu and circulating in USA (122 seqs) 2022-10-22T09:58:17Z 2022-10-23T00:59:31Z 2022-10-22T15:14:08Z
+1637 XBR designated Czech Republic (1/34), Canada-QC (6/34), UK-England (8/34), Israel (2/34), US-CA (17/34) This is the 6th BA.2.75 x BQ.1 recombinant cluster recently emerged, along with #1393 , #1425 , #1444, #1543 and #1567 . Again this recombinant has a breakpoint at spike.;Potential breakpoint: Spike NTD (22034-22189) One more BA.2.75 x BQ.1 recombinant with spike breakpoint (39 seqs, 2023-02-19) 2023-02-09T05:42:06Z 2023-02-22T03:56:33Z 2023-02-19T17:13:51Z
+1603 XBB.2.3 XBB.2.4 designated XBB.2 clusters with S:S486P 2023-01-31T13:07:20Z 2023-02-03T07:05:20Z NA
+1576 XBT designated Germany (66), Denmark (4), UK (3), Austria (1), Switzerland (1) Omicron/BA.5/22B,Omicron/BA.2.75/22D,Omicron/BA.5/22B 5184:9865,22600:22628 Recombinant of BA.2.75 -> BA.5.2.34, with the first breakpoint between 5183 and 9766, and the second breakpoint between 22577 and 22898. BA.5.2.34 -> BA.2.75 -> BA.5.2.34 sandwich recombinant with RBD breakpoint. [142 seqs, Europe] 2023-01-23T21:41:44Z 2023-02-19T17:51:16Z 2023-02-19T17:51:13Z
+1567 XBS designated Malaysia (2/23), Austria (2/23), UK-England (19/23) This is the 5th BA.2.75 x BQ.1 recombinant cluster recently emerged, along with #1393 , #1425 , #1444 and #1543 . Again this recombinant has a breakpoint at RBD. Yet another BA.2.75 x BQ.1. recombinant with RBD breakpoint (30 sequences as of 2023-02-12) 2023-01-21T11:52:00Z 2023-02-19T17:22:12Z 2023-02-19T17:22:07Z
+1551 XBK.1 designated XBK sublineage with S:P1162S mainly in Slovenia and Germany (>180 seq) 2023-01-15T03:24:21Z 2023-02-22T07:14:45Z 2023-02-19T16:33:39Z
+1532 XBL designated Omicron/CJ.1,Omicron/XBB/22F 5184:15737 Potential breakpoints: 406-3795 (nsp1-nsp3) and 5184-12443 (nsp3-nsp8);The BA.2.75 donor can't be narrowed down at all unless we assume that it provided G3446A. There is no large BA.2.75 branch with G3446A; there are a few small ones that could potentially be the donor but none have been found in Malaysia. One has been found in Kuwait so could be the donor if the recombination happened in the Middle East, but it is BA.2.75.2 so the second breakpoint would have to be between 5184 and 5191 which seems unlikely. XBB.1/BA.2.75/XBB.1 recombinant with S:F486P (8 seq, Malaysia) 2023-01-07T03:04:13Z 2023-02-10T17:27:16Z 2023-01-09T02:34:29Z
+1487 XBB.1.13 designated XBB.1 sublineage with S:F490P : 68 sequences - Usa , Czechia, Uk, Malaysia, Austria -EDITED as 09/02/23 2022-12-29T00:39:57Z 2023-02-10T13:16:41Z 2023-02-10T13:16:40Z
+1455 XBB.1.8 designated XBB.1 with S:186insSGG [14 sequences as of 2022-12-21, Denmark] 2022-12-21T16:47:45Z 2023-01-07T11:19:35Z 2023-01-07T11:19:35Z
+1441 XBM designated US (33 seqs), Canada (17 seqs), India (15 seqs), Denmark (5 seqs), Germany (1 seq), Maritius (1 seq), UK-Scotland (1 seq). Omicron/BA.2.76,Omicron/BA.5.2 22600:22916 Likely breakpoint: between 22601 and 22915 (Spike S1) Potential BA.2.76/BF.3 recombinant with spike changes from both donors (110 seqs as of 2023-01-06) 2022-12-17T01:39:48Z 2023-01-26T16:09:36Z 2023-01-09T03:40:33Z
+1440 XBQ designated Omicron/BA.5.2,Omicron/CJ.1 1628:3795 Third CJ.1/BA.5.2 recombinant after XBF and XBK - 68 Sequences defined by Orf1b:M454I 2022-12-16T15:43:54Z 2023-02-19T16:17:58Z 2023-01-17T21:57:51Z
+1428 XBB.1.6 designated USA, England, India, Australia XBB.1 sublineage with S:L216F (38 seqs from USA-CA, England, Australia, India) 2022-12-12T01:27:16Z 2022-12-15T23:47:07Z 2022-12-15T23:46:10Z
+1418 XBK designated Denmark 30, England and Norway 3, the Netherlands, Germany, Japan and USA 2, Sweden and Australia 1. Omicron/BA.5.2,Omicron/CJ.1 1628:3795 BA.5.2/CJ.1 recombinant, 46 samples from Denmark and 8 other countries 2022-12-08T14:40:19Z 2023-01-06T19:43:50Z 2022-12-15T00:45:03Z
+1393 XBP designated US-TX (3/6), Canada-NB (2/6), France (1/6) Omicron/BA.2.75/22D,Omicron/BA.5/22B 22191:22330 Potential breakpoint: Spike NTD (22193-22330) Potential BA.2.75/BQ.1 recombinant (41 seqs as of 2023-01-28) 2022-12-02T06:56:35Z 2023-01-30T18:36:36Z 2023-01-30T18:36:36Z
+1352 XBB.1.4.1 designated Denmark (23), Sweden (16), Norway (9), Iceland (1), France (1), Austria (1), Israel (1) Sublineage of XBB.1.4 with S:S673G (52 seq) 2022-11-18T22:42:38Z 2022-11-25T21:02:42Z 2022-11-25T16:48:04Z
+1296 XBN designated USA (CA, NY, VA, WV) Omicron/BA.2.75/22D,Omicron/XBB/22F 5184:12443 Potential breakpoint: Between ORF1a:P1640S (present) and ORF1a:N4060S (absent) BA.2.75 x XBB.3 recombinant [18 as of 2022-11-24, 15x US, 2x CH, 1x India] 2022-11-07T03:33:07Z 2023-01-22T17:21:35Z 2023-01-14T16:30:41Z
+1268 XBJ designated Australia, Austria, Italy, Japan, Singapore, US-TX and US-MN. Omicron/BA.2/21L,Omicron/BA.5/22B 23019:23039 Potential breakpoint: somewhere between Spike S1 and ORF3a (23015-25809) Potential BA.2.3.20/BA.5.2 recombinant (25 sequences as of 2022-12-01) 2022-10-31T15:39:30Z 2022-12-06T18:23:27Z 2022-12-01T17:35:03Z
+1259 XBF designated Omicron/BA.5/22B,Omicron/BA.2.75/22D 5184:9865 The breakpoint seems to be before 9866. The event seems to have happened very recently, maybe 1-2 months ago as there are no private mutations in the 20k bp downstream of 9866. BA.5.2.3 x CJ.1 (BM.1.1.1.1) recombinant [AUS-VIC, 9 sequences as of 2022-10-29] 2022-10-29T00:47:58Z 2023-02-14T02:34:45Z 2022-11-03T21:55:05Z
+1246 XBE designated Omicron/BA.5.2,Omicron/BA.5.3 22943:27011 Putative recombinant between BA.5.2+Orf1b:1050N+S:346T and BE.4+29868A spotted by @Alurqu and circulating in USA (122 seqs) 2022-10-22T09:58:17Z 2022-10-23T00:59:31Z 2022-10-22T15:14:08Z
+1229 XBH designated Nepal (2/17), Brunei (3/17), UK-England (1/17), France (5/17), Germany (1/17), Israel (2/17), Malaysia (1/17), Singapore ex-Nepal (1/17), Singapore local (1/17) Omicron/BA.2.3.17,Omicron/BA.2.75/22D 20236:22000 Potential breakpoint: somewhere between NSP12 and Spike NTD (15451-22000) Potential BA.2.3.17/BA.2.75.2 recombinant (66 seqs as of 2022-11-12) 2022-10-18T02:24:49Z 2022-11-16T17:34:46Z 2022-11-16T17:34:45Z
1130 XBC.1 designated XBC with S:L452M, ORF1a:S391F (6 seq. Philippines, USA, South Korea) 2022-09-28T06:55:38Z 2022-10-05T20:57:05Z 2022-10-03T13:13:39Z
-1100 XBC designated Omicron/BA.2/21L,Delta/21I,Omicron/BA.2/21L,Delta/21I 2791:4183,22579:22673,25470:25583 Delta (21I) / BA.2 recombinant (5 seq. Philippines, Austria) 2022-09-22T08:58:44Z 2022-10-05T21:49:22Z 2022-09-24T20:12:47Z
+1100 XBC designated Omicron/BA.2/21L,Delta/21I,Omicron/BA.2/21L,Delta/21I 2791:4183,22579:22673,25470:25583 Delta (21I) / BA.2 recombinant (5 seq. Philippines, Austria) 2022-09-22T08:58:44Z 2023-02-10T23:50:11Z 2022-09-24T20:12:47Z
1088 XBB.1 designated XBB sublineage with S:G252V (85 seq. Singapore, Bangladesh, Denmark, UK, Belgium, Australia, Austria as of 03.10.22) 2022-09-20T08:27:33Z 2022-10-04T12:28:53Z 2022-10-03T13:02:46Z
911 XAV designated Israel, Sweden, New Zealand, Indonesia, Metropolitan France, France-Reunion, UK (England, Scotland), Canada (ON), US (OH, NJ, NE, MA, CA) Omicron/BA.2/21L,Omicron/BA.5/22B 12161:22791 Likely breakpoint: 15960-17278 (NSP12/NSP13) Potential BA.2/BA.5 recombinant (53 sequences as of 2022-08-05) 2022-08-06T02:58:06Z 2022-08-23T13:21:12Z 2022-08-23T13:21:12Z
-895 XAW designated Russia (these 5 sequences are from 5 different patients as per age/sex: M63, F64, M67, F53 and M73) Delta/21J,Omicron/BA.2/21L,Delta/21J 20056:22577,27875:28310 Potential AY.122/BA.2 recombinant (7 seqs as of 2022-08-19, Russia) 2022-08-01T01:29:42Z 2022-10-19T15:02:42Z 2022-08-25T14:14:08Z
+896 XBG designated UK (4 in Scotland and 1 in Wales) Omicron/BA.2.76,Omicron/BA.5.2 22600:22916 Likely breakpoint: between 22601 and 22915 (Spike S1) Potential BA.2.76/BA.5.2 recombinant with spike changes from both donors (50 seqs as of 2022-11-12) 2022-08-01T08:26:50Z 2022-11-14T17:35:58Z 2022-11-14T17:35:58Z
+895 XAW designated Russia (these 5 sequences are from 5 different patients as per age/sex: M63, F64, M67, F53 and M73) Delta/21J,Omicron/BA.2/21L,Delta/21J 20056:22577,27875:28310 Potential AY.122/BA.2 recombinant (7 seqs as of 2022-08-19, Russia) 2022-08-01T01:29:42Z 2023-02-15T00:10:54Z 2022-08-25T14:14:08Z
894 XAU designated Omicron/BA.1/21K,Omicron/BA.2/21L 2833:4183 Likely breakpoint: 2834-4183 (NSP3) Potential BA.1.1/BA.2.9 recombinant with likely breakpoint at NSP3 (71 seqs as of 2022-07-30, Spain and other European countries) 2022-07-31T05:11:18Z 2022-08-23T11:10:25Z 2022-08-23T11:10:24Z
885 XAT designated Japan only Omicron/BA.2/21L,Omicron/BA.1/21K 26062:26528 Likely breakpoint: between ORF3a and M protein (26062-26528) Potential recombinant between BA.2.3.13 and BA.1 (67 seqs as of 2022-07-29 in Japan) 2022-07-29T03:31:23Z 2022-08-22T14:10:27Z 2022-08-22T14:10:27Z
882 XAS designated USA, Canada, Mexico, Australia, Chile, Colombia, France Omicron/BA.5/22B,Omicron/BA.2/21L 23041:26528 Likely breakpoint: 27,385-27,787 (based on presence of ORF6:D61L and lack of ORF7b:L11F) Potential BA.4/BA.5 recombinant with likely breakpoint between ORF6 and ORF7b, or BA.4 lineage missing ORF7b:L11F and N:P151S (65 sequences as of 2022-07-27, mostly USA) 2022-07-27T21:02:47Z 2022-08-22T13:16:37Z 2022-08-22T13:16:36Z
@@ -49,22 +67,44 @@ issue lineage status status_description countries parents_curated breakpoints_cu
263 XC designated Japan Delta/21J,Alpha/B.1.1.7/20I 26768:27637 Proposal to redesignate None as recombinant lineage X 2021-10-18T05:19:59Z 2021-11-29T17:13:32Z 2021-11-29T17:12:46Z
189 XB designated B.1.634,B.1.631 22917:23011 Proposal to redesignate B.1.631 as recombinant lineage XB 2021-08-16T18:57:43Z 2021-12-16T02:57:45Z 2021-11-22T11:39:05Z
63 XA designated B.1.177/20E,Alpha/B.1.1.7/20I 21256:21613 Their parental lineages are B.1.1.7 and B.1.177. The breakpoint location was identified as being between base 21255 and base 21764 in reference coordinates. Proposal to designate a recombinant lineage 2021-04-27T15:53:17Z 2021-05-05T13:43:39Z 2021-05-05T13:42:12Z
-1252 proposed1252 recombinant recombinant proposal Multiple different,independent BA.5.2+orf1b:1050N/ BA.5.2.1 recombinants with BQ.1 2022-10-25T17:18:54Z 2022-11-05T10:48:00Z NA
-1217 proposed1217 recombinant recombinant proposal Potential (XBB or BJ.1) and CJ.1 recombinant (2 seqs, Bangladesh/India, 2022-10-13) 2022-10-13T21:07:41Z 2022-11-03T12:56:20Z NA
+1674 proposed1674 recombinant recombinant proposal Potential BA.2.75/BA.5 recombinant (14 seq., Singapore, USA, UK, Austria) 2023-02-20T13:00:32Z 2023-02-21T10:24:43Z NA
+1668 proposed1668 recombinant recombinant proposal Chile (all 21) Breakpoint: Between 22304 and 22893 (S:248 and S:444);Pretty clear breakpoint somewhere in 22304-22893. In addition to ORF1a:M3621V and ORF1a:A4016T, 14/21 sequences have S:A262S. Peculiarly, two of the three nucleotides at S:262 are mutated, including the synonymous mutation T22348A, which has only appeared in 41 other sequences in the last six months. BF.31.1/BQ.1.10 Recombinant (21 seq, Feb 17) 2023-02-18T00:01:15Z 2023-02-18T10:00:21Z NA
+1667 proposed1667 recombinant recombinant proposal Potential breakpoint: 22034:22189 in RBD Potential BN.1.2/BQ.1.25 recombinant, similar to XBP but with Spike: R346T, S371F, S373P, S375F, T376A, D405N, R408S, K417N, N440K, K444T, L452R, N460K, S477N, T478K, E484A, F486V (50 sequences in Canada, USA, England...) 2023-02-17T20:46:48Z 2023-02-19T17:56:31Z 2023-02-19T17:56:30Z
+1666 proposed1666 recombinant recombinant proposal Possible 5' tip recombinant (BA.5.2) in EF.1.3 (Denmark, ~240 sequences) 2023-02-17T19:18:23Z 2023-02-19T17:55:39Z NA
+1647 proposed1647 recombinant recombinant proposal BA.4.6 is the first 2/3, BA.5.1 the last 1/3, with breakpoints either between S:346-S:658 or after. Recombinants of BA.4.6 and BA.5.1 2023-02-11T14:33:38Z 2023-02-11T14:33:53Z NA
+1589 proposed1589 recombinant recombinant proposal Australia (all in NSW) Potential breakpoint: NSP3, between AA 1307 and 2104 Potential BA.2.75 x XBC.1.1 recombinant (5 sequences as of 2023-01-28) 2023-01-29T11:54:09Z 2023-02-10T16:32:41Z NA
+1332 proposed1332 recombinant recombinant proposal Canada (37/38 from Ontario and 1/38 from Quebec) Potential breakpoint: somewhere between E and M proteins (26278-27258) Potential BA.2.75.2/BF.27 recombinant (40 sequences as of 2022-11-14) 2022-11-14T02:48:27Z 2022-12-26T01:44:14Z 2022-12-26T01:44:14Z
+1305 proposed1305 recombinant recombinant proposal Omicron/BA.2.75/22D,Omicron/BA.5.2 27013:27037 It is a recombinant between BA.2.75.2 and BA.5.2.1 (or sublineages of those parents). The breakpoint is around 27013:27037 bp from the reference strain (Wuhan). Currently it is only circulating in Canada. BA.2.75.2 and BA.5.2.1 recombinant lineage in Ontario, Canada 2022-11-08T21:54:40Z 2022-11-09T13:36:55Z NA
+1252 proposed1252 recombinant recombinant proposal Multiple different,independent BA.5.2+orf1b:1050N/ BA.5.2.1 recombinants with BQ.1 2022-10-25T17:18:54Z 2022-11-16T20:56:52Z 2022-11-16T20:56:52Z
+1217 proposed1217 recombinant recombinant proposal Potential (XBB or BJ.1) and CJ.1 recombinant (2 seqs, Bangladesh/India, 2022-10-13) 2022-10-13T21:07:41Z 2022-11-24T12:46:38Z 2022-11-24T12:46:37Z
1006 proposed1006 recombinant recombinant proposal Omicron/BA.5/22B,Omicron/BA.2/21L,Omicron/BA.5/22B 26532:27381,27810:29665 Likely breakpoint 1: between 26532 and 27381;Likely breakpoint 2: between 27810 and 29665 Potential BA.5/BA.2/BA.5.1 recombinant (90 sequences as of 2022-08-29, mainly UK) 2022-08-30T10:05:00Z 2022-11-01T20:50:54Z 2022-11-01T20:50:54Z
820 proposed820 recombinant recombinant proposal Just Denmark for the fast growing main cluster Omicron/BA.5/22B 28722:28723 Sublineage/recombinant of a (2nd generation?) BA.5.2.1 plus N:P151S and S:N185Y with BA.4/BA.2 in Denmark 2022-07-04T10:50:08Z 2022-07-25T13:58:14Z NA
781 proposed781 recombinant recombinant proposal Omicron/BA.1/21K,Omicron/BA.2/21L 17411:19954 Likely breakpoint: between 17410 and 19995 (NSP13 or NSP14 or NSP15, ORF1b).;We noticed the same breakpoint as in XM #757. Potential BA.1/BA.2 Recombinant in Brazil, with a likely breakpoint at NSP13-15 (10 Seqs as of 2022-06-22) 2022-06-22T20:56:10Z 2022-08-08T14:10:03Z 2022-08-08T14:08:37Z
608 proposed608 recombinant recombinant proposal Germany (NW, RP, HH), Israel, Norway, UK (England) Omicron/BA.2/21L,Omicron/BA.1/21K 24504:26059 - It has interesting double breakpoints and dual ORF3a substitutions;;Likely breakpoint 1: 24506-26048 (Spike S2/ORF3a);Likely breakpoint 2: 27385-29509 (ORF6/N protein), with S2M deletion. Potential BA.2/BA.1/BA.2 Recombinant with Likely Double Breakpoints (29 Seqs as of 2022-05-06 in Germany, Norway, Israel and UK) 2022-05-07T03:01:50Z 2022-07-21T23:10:00Z 2022-07-21T23:10:00Z
514 proposed514 recombinant recombinant proposal Uncertain (travellers arrived in Hong Kong from Germany via the Netherlands) Omicron/BA.1/21K,Omicron/BA.2/21L 20055:21618 Breakpoint: 20055-21618;We detected a recombinant between Omicron subvariants BA.1 and BA.2. It was detected in early Feb from two epidemiologically linked cases. This recombinant has a breakpoint at around the 5' end of spike (20055-21618).;We used Patient 2 sample to clone a RT-PCR product (~2.2k bp) spanning the recombination breakpoint. Both BA.1-specific (19955C/20055A) and BA.2-specific (21618T/21633-21641del) SNPs can be detected in the same plasmid clone, confirming that Patients 1 and 2 were indeed infected by a BA.1/BA.2 recombinant virus. BA.2/BA.1 Recombinant with breakpoint at 20055-21618 detected in travellers arriving in Hong Kong, February 2022 2022-04-02T12:14:42Z 2022-10-12T07:43:47Z 2022-10-12T07:43:47Z
-1296 proposed1296 monitor currently too small, watch for future developments USA (CA, NY, VA, WV) Omicron/BA.2.75/22D,Omicron/XBB/22F 5184:12443 Potential breakpoint: Between ORF1a:P1640S (present) and ORF1a:N4060S (absent) Potential BA.2.75 x XBB.3 recombinant in the US 2022-11-07T03:33:07Z 2022-11-07T19:03:32Z NA
+1669 proposed1669 monitor currently too small, watch for future developments all sequences are from Australia, but from 4 different states, including NSW (6/11), VIC (2/11), SA (2/11) and QLD (1/11). Potential breakpoint: NSP16 or Spike RBD (21055-22916) BR.2.1 x XBF recombinant (11 sequences as of 2023-02-17, from 4 states of Australia) 2023-02-18T04:21:25Z 2023-02-19T17:59:09Z NA
+1653 proposed1653 monitor currently too small, watch for future developments Possible recombinant lineage between BR.2.1 and CJ.1 (or XBF or XBK) defined by ORF1b:R2613H ,S:I486P S:L452L(rev), S:F490S ,Orf8:S82T, Orf8:S67S (rev) - 11 sequences 2023-02-13T15:15:49Z 2023-02-18T09:16:11Z 2023-02-18T09:15:47Z
+1644 proposed1644 monitor currently too small, watch for future developments Potential XBB.1.5/BA.5 recombinant (5 seq., Canada) 2023-02-10T07:42:27Z 2023-02-10T15:17:55Z NA
+1642 proposed1642 monitor currently too small, watch for future developments Breakpoint somewhere in ORF1b (>17k) or Spike (<23k). Potentially first mainland China recombinant: BA.5.2.48 x BF.7.14 [2 seq, Anhui] 2023-02-09T22:31:01Z 2023-02-19T09:47:33Z NA
+1641 proposed1641 monitor currently too small, watch for future developments Denmark, 5 Breakpoint: between nucleotide 9866 and 12160 Description: BA.5.11 sublineage(#1640)/XBK recombinant, 6 samples from Denmark 2023-02-09T17:25:31Z 2023-02-21T13:33:54Z NA
+1614 XBB.1.5.10 monitor currently too small, watch for future developments XBB.1.5 with S:F456L and Orf1b:A1643V apparently growing fastly in North Carolina , USA (48 sequences) 2023-02-03T13:15:44Z 2023-02-10T15:13:44Z 2023-02-10T13:05:22Z
+1543 proposed1543 monitor currently too small, watch for future developments Singapore ex-Vietnam (1/5), Singapore local case (2/5), Germany (1/5), US-CA (1/5) Another BA.2.75/BQ.1 recombinant with RBD breakpoint (6 sequences as of 2022-01-13) 2023-01-11T00:59:34Z 2023-01-13T13:45:35Z NA
+1535 XBB.2.3 monitor currently too small, watch for future developments XBB.2 with S:F486P,S:P521S - 11 sequences USA, India as 25-01-23 2023-01-08T08:52:54Z 2023-01-31T14:38:05Z 2023-01-31T13:30:45Z
+1505 proposed1505 monitor currently too small, watch for future developments US-NY Potential breakpoint: ORF3a/E (25417-26274) Potential XBB.1.5/BQ.1.14 recombinant (8 sequences as of 2023-02-17) 2023-01-01T03:16:29Z 2023-02-18T04:29:53Z NA
+1490 XBB.1.11 XBB.1.11.1 monitor currently too small, watch for future developments Another XBB.1 sublineage with S:F486P (12 seq, mainly SE Asia) 2022-12-29T17:58:22Z 2023-01-31T14:37:03Z 2023-01-31T14:36:52Z
+1460 proposed1460 monitor currently too small, watch for future developments I've come across a BA.5 - XBB.1 recombinant with BA.5 5' end and breakpoint before Spike, around nt pos 20000. CR.1 (BA.5.2.18.1) x XBB.1 recombinant [5 seq, 2x UK, 1x US, 1x FR, 1x DK] 2022-12-22T16:43:04Z 2023-02-13T09:39:10Z NA
+1444 proposed1444 monitor currently too small, watch for future developments Korea (1/8), France (1/8), Germany (1/8), US-KS (1/8), New Caledocia (1/8), Italy (3/8) Omicron/BA.2.75/22D,Omicron/BA.5/22B 22600:22628 Potential breakpoint: Spike NTD (22580-22939) Another BA.2.75/BQ.1 recombinant similar to #1393 and #1425 (26 seqs as of 2023-02-10) 2022-12-19T04:43:24Z 2023-02-10T16:37:13Z NA
+1425 proposed1425 monitor currently too small, watch for future developments Japan-Fukushima (3/6), Malaysia-Selangor (2/6), Australia-Sydney (1/6), Omicron/CJ.1,Omicron/BA.5.3,Omicron/CJ.1 22578:22892,25417:26274 Potential breakpoint: Spike NTD (22577, 22893) and (25416, 26275) BA.2.75 x BQ.1 x BA.2.75 recombinant [27 seqs, many countries, continents] 2022-12-10T21:24:00Z 2023-02-20T15:26:53Z NA
+1362 proposed1362 monitor currently too small, watch for future developments Potential BA.2/BA.5 recombinant (6 seqs., UK) 2022-11-23T11:05:05Z 2023-02-06T15:40:27Z 2023-02-06T15:40:27Z
+1360 proposed1360 monitor currently too small, watch for future developments France BA.2.75/BA.5 Recombinant with S:K986Q & other private mutations (France, 8 seq) 2022-11-22T20:51:49Z 2022-12-17T15:58:40Z 2022-12-17T15:58:40Z
+1340 proposed1340 monitor currently too small, watch for future developments Singapore Omicron/BA.2.75/22D,Omicron/XBB/22F 20236:21809 Potential breakpoint: somewhere between ORF1b and Spike NTD (16345 to 21808). Potential BN.1.3/XBB.1 recombinant with multiple private mutations in N protein (2 sequences as of 2022-11-14) 2022-11-15T08:42:38Z 2022-12-26T01:44:56Z 2022-12-26T01:44:55Z
1255 XAY.1.1 monitor currently too small, watch for future developments XAY.1 with S:346T (1 sequence, South Africa) 2022-10-26T18:56:28Z 2022-11-02T23:44:50Z 2022-11-02T23:44:50Z
-1218 proposed1218 monitor currently too small, watch for future developments I cam across a few sequences that had a Spike very similar to XBB - but with breakpoint a little bit different, as it contains the BA.2.75 S:257S Potential recombinant, similar to XBB, maybe similar parents or even involving it 2022-10-15T18:36:41Z 2022-10-15T18:36:51Z NA
+1218 proposed1218 monitor currently too small, watch for future developments I cam across a few sequences that had a Spike very similar to XBB - but with breakpoint a little bit different, as it contains the BA.2.75 S:257S Potential recombinant, similar to XBB, maybe similar parents or even involving it 2022-10-15T18:36:41Z 2023-02-19T17:52:22Z 2023-02-19T17:52:22Z
1208 XAY.1 monitor currently too small, watch for future developments XAY sublineage with S:D253G (6 seq., South Africa, USA) 2022-10-12T07:10:20Z 2022-10-15T23:49:25Z 2022-10-15T23:49:25Z
-1185 proposed1185 monitor currently too small, watch for future developments Looks to be a recombinant between BA.2/BA.5, breakpoint between Spike 70 and 452 with the addition of S:T547K and S:S1147L Potential BA.2/BA.5 recombinant lineage with S:T547K and S:S1147L (13 Examples, mainly Belgium) 2022-10-07T14:58:07Z 2022-10-11T11:51:08Z NA
+1185 proposed1185 monitor currently too small, watch for future developments Looks to be a recombinant between BA.2/BA.5, breakpoint between Spike 70 and 452 with the addition of S:T547K and S:S1147L Potential BA.2/BA.5 recombinant lineage with S:T547K and S:S1147L (13 Examples, mainly Belgium) 2022-10-07T14:58:07Z 2022-12-05T11:02:10Z 2022-12-05T11:02:10Z
1175 XBB.4 monitor currently too small, watch for future developments XBB with S:444R [2 seqs, India] 2022-10-06T17:53:17Z 2022-10-20T19:08:30Z 2022-10-20T19:08:19Z
-1139 proposed1139 monitor currently too small, watch for future developments All 10 sequences are from Japan border control, among which 9 patients have a recent travel history from both Taiwan and mainland China (and the other one has a travel history from Taiwan only), therefore it's currently unclear if it's been circulating in either Taiwan, or mainland China, or both, or even neither. Omicron/BA.2/21L,Omicron/BA.5/22B 12161:22791 Potential breakpoint: between spike 97 and 452 Potential BA.2.3.7/BA.5.1 recombinants (10 sequences as of 2022-09-29) 2022-09-29T12:42:30Z 2022-10-18T02:27:49Z NA
+1139 proposed1139 monitor currently too small, watch for future developments All 10 sequences are from Japan border control, among which 9 patients have a recent travel history from both Taiwan and mainland China (and the other one has a travel history from Taiwan only), therefore it's currently unclear if it's been circulating in either Taiwan, or mainland China, or both, or even neither. Omicron/BA.2/21L,Omicron/BA.5/22B 12161:22791 Potential breakpoint: between spike 97 and 452 Potential BA.2.3.7/BA.5.1 recombinants (10 sequences as of 2022-09-29) 2022-09-29T12:42:30Z 2022-11-15T15:58:24Z 2022-11-15T15:58:23Z
1138 proposed1138 monitor currently too small, watch for future developments Putative (BE.4=BA.5.3.1+Orf1a:T3308N)/(BA.5.2+Orf1b:1050N+Orf7a:T39I) recombinant sublineage with S:N460K , S:K444N 4 seqs (Usa, France , Bangladesh) 2022-09-29T09:18:42Z 2022-10-11T18:21:38Z 2022-10-11T18:21:37Z
-896 proposed896 monitor currently too small, watch for future developments UK (4 in Scotland and 1 in Wales) Omicron/BA.2/21L,Omicron/BA.5/22B 22601:22915 Likely breakpoint: between 22601 and 22915 (Spike S1) Potential BA.2.76/BA.5.2 recombinant with spike changes from both donors (33 seqs as of 2022-11-01, UK) 2022-08-01T08:26:50Z 2022-11-01T14:49:56Z NA
484 proposed484 monitor currently too small, watch for future developments Delta/21J,Omicron/BA.2/21L 21619:21761 It looks clean to me, with a single breakpoint at the start of S1. Potential Delta-BA.2 recombinant from Karnataka, India (singlet, 2022-02-11) 2022-03-24T23:20:55Z 2022-05-11T17:08:57Z 2022-05-11T17:08:57Z
701 proposed701 not accepted A proposal for a new lineage has not been accepted Possible recombinant BA.2/A.5 appeared in Asturias. Spain 2022-06-02T17:56:00Z 2022-06-13T11:20:04Z 2022-06-13T11:19:56Z
557 proposed557 not accepted A proposal for a new lineage has not been accepted Canada, Quebec Omicron/BA.1/21K,Omicron/BA.2/21L 11538:12879 Potential BA.1/BA.2 Recombinant with Likely Breakpoint at NSP6-NSP9 (5 Seqs in Canada, Quebec as of 2022-04-13) 2022-04-13T16:52:51Z 2022-06-29T15:42:26Z 2022-04-19T15:28:12Z
@@ -82,17 +122,26 @@ issue lineage status status_description countries parents_curated breakpoints_cu
439 proposed439 not accepted A proposal for a new lineage has not been accepted Delta/21J,Omicron/BA.1/21K 21988:22577 Breakpoint: between Spike NTD/RBD nuc 22199-22576 Potential US East Coast BA.1.1/AY.119 recombinant (7 seq) [potentially the one monitored by UKHSA] 2022-02-14T19:09:24Z 2022-05-11T14:33:17Z 2022-05-11T14:33:09Z
422 proposed422 not accepted A proposal for a new lineage has not been accepted Possible English recombinant descended from Omicron and Delta (UKHSA signal under monitoring) 2022-01-31T07:44:42Z 2022-05-11T15:09:41Z 2022-05-11T15:09:34Z
210 proposed210 not accepted A proposal for a new lineage has not been accepted Korea (12 sequences); Korea (9 sequences); Spain (3 sequences), Belgium (1), Germany (1), France (1), Korea ex-UAE (1) 3 small clades with interesting (recombinant-look-alike) mutations 2021-09-06T02:04:47Z 2021-10-12T17:14:00Z 2021-10-12T17:14:00Z
+1651 proposed1651 recombinant sublineage Australia (15), Singapore (2) XBF Sublineage with S:P621S (24 seq, Feb 20) 2023-02-13T11:31:18Z 2023-02-21T01:41:41Z NA
+1622 proposed1622 recombinant sublineage XBC.1 with S:G184S, ORF1a:P3952S, ORF1a:V4072A (42 seq., Australia, Singapore) 2023-02-06T08:41:51Z 2023-02-20T14:24:20Z 2023-02-20T14:24:20Z
+1602 proposed1602 recombinant sublineage RFC: XBB.1.9 with S:S486P 2023-01-31T11:57:44Z 2023-02-10T01:14:10Z 2023-02-03T06:48:08Z
+1562 proposed1562 recombinant sublineage XBC.2 with S:F486L circulating in Brunei (28 sequences ) 2023-01-18T20:34:33Z 2023-01-30T14:25:04Z 2023-01-30T14:25:04Z
+1553 proposed1553 recombinant sublineage XAY.1 sublineage with ORF1a:V86I (46 seq. Slovenia, Germany, Croatia, US) 2023-01-15T03:49:42Z 2023-02-16T21:36:03Z NA
+1545 proposed1545 correction Highlight an error in the description or definition Correction - Recombinants with duplicate references on alias_key.json 2023-01-11T20:10:07Z 2023-01-12T04:24:22Z 2023-01-12T04:24:22Z
+1538 proposed1538 recombinant sublineage Australia - Western, Victoria, Tasmania XBC.2 sublineage with S:K444T [17 seqs, Australia] 2023-01-09T22:24:50Z 2023-02-14T14:49:59Z NA
+1459 XBB.6.1 XBB proposed sublineage of XBB XBB with S:486P from US east coast - artefact or homoplasy? [9 sequences US, 1 AUS] 2022-12-22T15:44:32Z 2023-01-31T13:34:11Z 2023-01-09T04:19:08Z
+1437 proposed1437 since WHO declared XBB as variant under monitoring (VUM), I tried to find the breakpoint and the p-value.;could find a breakpoint at position 28370 and 29836 with a siginificant p-value. validation of recombinant lineages 2022-12-15T11:59:54Z 2022-12-20T22:53:57Z 2022-12-15T17:47:01Z
+1367 proposed1367 recombinant sublineage XBC.1 sublineage with S:G476S circulating in Australia (8 sequences - 4 states NSW, VIC, QLD, TAS) 2022-11-25T15:29:55Z 2022-12-04T21:42:18Z 2022-12-04T21:42:18Z
+1330 proposed1330 recombinant sublineage About 19 countries Sublineage(s) of XBB with S:H146K (~135 seq, ~19 countries) 2022-11-13T14:04:56Z 2022-12-19T06:35:15Z 2022-12-17T15:33:04Z
1273 proposed1273 Should recombinant alias keys all use unaliased names for parity 2022-11-01T16:06:33Z 2022-11-02T13:16:47Z 2022-11-01T16:20:52Z
-1268 proposed1268 recommended Recommended for designation by pango team member Australia, Austria, Italy, Japan, Singapore, US-TX and US-MN. Omicron/BA.2/21L,Omicron/BA.5/22B 23019:23039 Potential breakpoint: somewhere between Spike S1 and ORF3a (23015-25809) Potential BA.2.3.20/BA.5.2 recombinant (11 sequences as of 2022-11-07) 2022-10-31T15:39:30Z 2022-11-08T04:32:54Z NA
-1260 proposed1260 recombinant sublineage XBD sublineage with S:N188S circulating in Hong Kong 11 sequences (Nine of them have also S:P1143L) 2022-10-29T19:11:28Z 2022-11-05T19:21:03Z 2022-11-05T19:21:03Z
-1229 proposed1229 recommended Recommended for designation by pango team member Nepal (2/17), Brunei (3/17), UK-England (1/17), France (5/17), Germany (1/17), Israel (2/17), Malaysia (1/17), Singapore ex-Nepal (1/17), Singapore local (1/17) BA.2.3.17,Omicron/BA.2.75/22D 15452:22000 Potential breakpoint: somewhere between NSP12 and Spike NTD (15451-22000) Potential BA.2.3.17/BA.2.75.2 recombinant (53 seqs as of 2022-11-07) 2022-10-18T02:24:49Z 2022-11-08T04:34:07Z NA
+1260 proposed1260 recombinant sublineage XBD sublineage with S:N188S circulating in Hong Kong 24 sequences 2022-10-29T19:11:28Z 2022-12-18T14:04:51Z 2022-12-18T14:04:51Z
1222 proposed1222 recombinant sublineage Potential XBB sublineage with ORF1a:G82D 2022-10-16T21:39:36Z 2022-10-20T19:30:12Z 2022-10-20T19:30:12Z
1201 proposed1201 recombinant sublineage Collection of XBB Spike diversity 2022-10-10T12:39:28Z 2022-11-05T07:01:25Z NA
1188 proposed1188 recombinant sublineage Collection of XBB.1 Spike diversity 2022-10-07T20:33:13Z 2022-10-11T08:51:38Z 2022-10-11T08:51:38Z
-1137 XBD recommended Recommended for designation by pango team member Omicron/BA.2.75/22D,Omicron/BA.5/22B 23020:25415 Potential breakpoint: between spike 486 and 1020. Potential BA.2.75.2/BF.5 recombinants (27 sequences as of 2022-10-11) 2022-09-29T06:31:03Z 2022-10-12T14:29:29Z 2022-10-12T12:53:19Z
-1119 proposed1119 recombinant sublineage XAW sublineage with S:D215Y (19 seq. Russia as of 2022-10-17) 2022-09-26T07:21:21Z 2022-10-17T10:20:47Z NA
+1137 XBD recommended Recommended for designation by pango team member Omicron/BA.2.75/22D,Omicron/BA.5/22B 23020:23030 Potential breakpoint: between spike 486 and 1020. Potential BA.2.75.2/BF.5 recombinants (27 sequences as of 2022-10-11) 2022-09-29T06:31:03Z 2022-10-12T14:29:29Z 2022-10-12T12:53:19Z
+1119 proposed1119 recombinant sublineage XAW sublineage with S:D215Y (19 seq. Russia as of 2022-10-17) 2022-09-26T07:21:21Z 2022-11-09T14:59:32Z 2022-11-09T14:59:31Z
1103 proposed1103 duplicate This is an overlap or duplication of an existing proposal A different Delta recombinant from St Petersburg, Russia? 2022-09-23T21:49:46Z 2022-10-11T11:30:22Z 2022-10-11T11:30:22Z
-1058 XBB accepted A proposal for a new lineage has been accepted and will be designated. BA.2.10,Omicron/BA.2.75/22D 22332:22576 BJ.1/BM.1.1.1 (=BA.2.75.3.1.1.1) recombinant with breakpoint in S1 [>=5 sequences, 3x Singapore, 2x US as of 2022-09-12] 2022-09-12T16:29:54Z 2022-10-05T10:17:35Z 2022-09-17T20:11:09Z
+1058 XBB accepted A proposal for a new lineage has been accepted and will be designated. Omicron/BA.2.10,Omicron/BA.2.75/22D 22332:22576 BJ.1/BM.1.1.1 (=BA.2.75.3.1.1.1) recombinant with breakpoint in S1 [>=5 sequences, 3x Singapore, 2x US as of 2022-09-12] 2022-09-12T16:29:54Z 2022-10-05T10:17:35Z 2022-09-17T20:11:09Z
646 proposed646 correction Highlight an error in the description or definition India/RJ-SMS-ICMR-INSACOG-TS-11049/2022 has a breakpoint between 12880 and 13195;The six others have their breakpoint between 5386 and 8393 Two differents recombinants lineage for XU in lineages.csv 2022-05-17T07:29:13Z 2022-05-17T18:18:25Z 2022-05-17T18:18:25Z
559 proposed559 Omicron/BA.1/21K,Omicron/BA.2/21L 15714:17410 Likely breakpoint: between 15714 and 17410 (ORF1b- NSP12 and NSP13);The sample hCoV-19/Brazil/SP-DASA828822284657/2022 was classified as XM by nextclade, however it seemed to have a different breakpoint with a 21L labeled mutation at 17410:;We used Lena’s amazing tool (https://github.com/lenaschimmel/sc2rf) to visualize our sequences and five samples from Malaysia, Denmark, and India (EPI_ISL_10369263, EPI_ISL_11273668, EPI_ISL_11902802, EPI_ISL_11902839, EPI_ISL_11902849), that clustered together in the tree and seems to have the same breakpoint. Additionally, two XM, XJ and XE samples were added to sc2rf output:;The samples may share the same breakpoint and the Brazilian sample seems to harbor a BA.1.14 profile at ORF1A. We will monitor if more samples will emerge. Potential recombinant BA.1/BA.2 circulating in Brazil, Malaysia, Denmark and India 2022-04-16T23:34:09Z 2022-04-18T10:13:51Z 2022-04-18T10:13:51Z
493 proposed493 Possible misclassification of recombinant Omicron lineage XK as XE by Nextclade_pango lineage 2022-03-31T15:10:13Z 2022-03-31T15:15:30Z 2022-03-31T15:15:27Z
@@ -105,3 +154,7 @@ issue lineage status status_description countries parents_curated breakpoints_cu
808 proposed808-2 B.1.438.1,Omicron/BA.1/21K 18164:18876
844 XAY Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L 6403:8985,9867:10197,12881:15450,21847:22199,24470:24999,27385:27637
844 XBA Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J 2791:4180,21847:22199,24470:24999
+1058 XBB_ARTICv4.1 Omicron/BA.2.10,Omicron/BA.2.75/22D 22191:22576
+1440 proposed1440 Omicron/BA.5.2,Omicron/CJ.1 1628:3795
+1576 proposed1576 Omicron/BA.5/22B,Omicron/BA.2.75/22D,Omicron/BA.5/22B 5184:9865,22600:22628
+844 XAY_dropout Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L,Delta/21J,Omicron/BA.2/21L 6403:8985,9867:10197,12881:15450,20056:22199,24470:24999,27385:27637
diff --git a/resources/tree.nwk b/resources/tree.nwk
new file mode 100644
index 0000000..66f5df6
--- /dev/null
+++ b/resources/tree.nwk
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517:1.00000,B.1.518:1.00000,B.1.520:1.00000,B.1.521:1.00000,B.1.523:1.00000,B.1.524:1.00000,B.1.525:1.00000,B.1.526:1.00000,B.1.527:1.00000,B.1.528:1.00000,B.1.529:1.00000,B.1.530:1.00000,B.1.531:1.00000,B.1.532:1.00000,B.1.533:1.00000,B.1.534:1.00000,B.1.535:1.00000,B.1.536:1.00000,B.1.537:1.00000,B.1.538:1.00000,B.1.539:1.00000,B.1.540:1.00000,B.1.541:1.00000,B.1.542:1.00000,B.1.543:1.00000,B.1.544:1.00000,B.1.545:1.00000,B.1.546:1.00000,B.1.547:1.00000,B.1.548:1.00000,B.1.549:1.00000,B.1.550:1.00000,B.1.551:1.00000,B.1.552:1.00000,B.1.554:1.00000,B.1.555:1.00000,B.1.556:1.00000,B.1.557:1.00000,B.1.558:1.00000,B.1.559:1.00000,B.1.560:1.00000,B.1.561:1.00000,B.1.562:1.00000,B.1.563:1.00000,(B.1.564.1:1.00000)B.1.564:1.00000,B.1.565:1.00000,B.1.566:1.00000,B.1.567:1.00000,B.1.568:1.00000,B.1.569:1.00000,B.1.570:1.00000,B.1.571:1.00000,B.1.572:1.00000,B.1.573:1.00000,B.1.574:1.00000,(B.1.575.1:1.00000,B.1.575.2:1.00000)B.1.575:1.00000,B.1.576:1.00000,B.1.577:1.00000,B.1.578:1.00000,B.1.579:1.00000,B.1.580:1.00000,B.1.581:1.00000,B.1.582:1.00000,B.1.585:1.00000,B.1.586:1.00000,B.1.587:1.00000,(B.1.588.1:1.00000)B.1.588:1.00000,B.1.589:1.00000,B.1.590:1.00000,B.1.591:1.00000,B.1.592:1.00000,B.1.593:1.00000,B.1.594:1.00000,(B.1.595.1:1.00000,B.1.595.2:1.00000,B.1.595.3:1.00000,B.1.595.4:1.00000)B.1.595:1.00000,(B.1.596.1:1.00000)B.1.596:1.00000,B.1.597:1.00000,B.1.598:1.00000,B.1.599:1.00000,B.1.600:1.00000,B.1.601:1.00000,B.1.602:1.00000,B.1.603:1.00000,B.1.604:1.00000,B.1.605:1.00000,B.1.606:1.00000,B.1.607:1.00000,B.1.609:1.00000,B.1.610:1.00000,B.1.611:1.00000,B.1.612:1.00000,B.1.613:1.00000,B.1.614:1.00000,B.1.615:1.00000,B.1.616:1.00000,(B.1.617.1:1.00000,(AY.1:1.00000,AY.2:1.00000,(AY.3.1:1.00000,AY.3.2:1.00000,AY.3.3:1.00000,AY.3.4:1.00000)AY.3:1.00000,(AY.4.1:1.00000,(AY.4.2.1:1.00000,AY.4.2.2:1.00000,AY.4.2.3:1.00000,AY.4.2.4:1.00000,AY.4.2.5:1.00000)AY.4.2:1.00000,AY.4.3:1.00000,AY.4.4:1.00000,AY.4.5:1.00000,AY.4.6:1.00000,AY.4.7:1.00000,AY.4.8:1.00000,AY.4.9:1.00000,AY.4.10:1.00000,AY.4.11:1.00000,AY.4.12:1.00000,AY.4.13:1.00000,AY.4.14:1.00000,AY.4.15:1.00000,AY.4.16:1.00000,AY.4.17:1.00000)AY.4:1.00000,(AY.5.1:1.00000,AY.5.2:1.00000,AY.5.3:1.00000,AY.5.4:1.00000,AY.5.5:1.00000,AY.5.6:1.00000,AY.5.7:1.00000)AY.5:1.00000,AY.6:1.00000,(AY.7.1:1.00000,AY.7.2:1.00000)AY.7:1.00000,AY.8:1.00000,((AY.9.2.1:1.00000,AY.9.2.2:1.00000)AY.9.2:1.00000)AY.9:1.00000,AY.10:1.00000,AY.11:1.00000,AY.13:1.00000,AY.14:1.00000,AY.15:1.00000,(AY.16.1:1.00000)AY.16:1.00000,AY.17:1.00000,AY.18:1.00000,AY.19:1.00000,(AY.20.1:1.00000)AY.20:1.00000,AY.21:1.00000,AY.22:1.00000,(AY.23.1:1.00000,AY.23.2:1.00000)AY.23:1.00000,(AY.24.1:1.00000)AY.24:1.00000,((AY.25.1.1:1.00000,AY.25.1.2:1.00000)AY.25.1:1.00000,AY.25.2:1.00000,AY.25.3:1.00000)AY.25:1.00000,(AY.26.1:1.00000)AY.26:1.00000,AY.27:1.00000,AY.28:1.00000,(AY.29.1:1.00000,AY.29.2:1.00000)AY.29:1.00000,AY.30:1.00000,AY.31:1.00000,AY.32:1.00000,(AY.33.1:1.00000,AY.33.2:1.00000)AY.33:1.00000,((AY.34.1.1:1.00000)AY.34.1:1.00000,AY.34.2:1.00000)AY.34:1.00000,AY.35:1.00000,(AY.36.1:1.00000)AY.36:1.00000,AY.37:1.00000,AY.38:1.00000,((AY.39.1.1:1.00000,AY.39.1.2:1.00000,AY.39.1.3:1.00000,AY.39.1.4:1.00000)AY.39.1:1.00000,AY.39.2:1.00000,AY.39.3:1.00000)AY.39:1.00000,AY.40:1.00000,AY.41:1.00000,(AY.42.1:1.00000)AY.42:1.00000,(AY.43.1:1.00000,AY.43.2:1.00000,AY.43.3:1.00000,AY.43.4:1.00000,AY.43.5:1.00000,AY.43.6:1.00000,AY.43.7:1.00000,AY.43.8:1.00000,AY.43.9:1.00000)AY.43:1.00000,AY.44:1.00000,AY.45:1.00000,(AY.46.1:1.00000,AY.46.2:1.00000,AY.46.3:1.00000,AY.46.4:1.00000,AY.46.5:1.00000,(AY.46.6.1:1.00000)AY.46.6:1.00000)AY.46:1.00000,AY.47:1.00000,AY.48:1.00000,AY.49:1.00000,AY.50:1.00000,AY.51:1.00000,AY.52:1.00000,AY.53:1.00000,AY.54:1.00000,AY.55:1.00000,AY.56:1.00000,AY.57:1.00000,AY.58:1.00000,AY.59:1.00000,AY.60:1.00000,AY.61:1.00000,AY.62:1.00000,AY.63:1.00000,AY.64:1.00000,AY.65:1.00000,AY.66:1.00000,AY.67:1.00000,AY.68:1.00000,AY.69:1.00000,AY.70:1.00000,AY.71:1.00000,AY.72:1.00000,AY.73:1.00000,AY.74:1.00000,(AY.75.2:1.00000,AY.75.3:1.00000)AY.75:1.00000,AY.76:1.00000,AY.77:1.00000,AY.78:1.00000,AY.79:1.00000,AY.80:1.00000,AY.81:1.00000,AY.82:1.00000,AY.83:1.00000,AY.84:1.00000,AY.85:1.00000,AY.86:1.00000,AY.87:1.00000,AY.88:1.00000,AY.90:1.00000,(AY.91.1:1.00000)AY.91:1.00000,AY.92:1.00000,AY.93:1.00000,AY.94:1.00000,AY.95:1.00000,((AY.98.1.1:1.00000)AY.98.1:1.00000)AY.98:1.00000,(AY.99.1:1.00000,AY.99.2:1.00000)AY.99:1.00000,AY.100:1.00000,AY.101:1.00000,(AY.102.1:1.00000,AY.102.2:1.00000)AY.102:1.00000,(AY.103.1:1.00000,AY.103.2:1.00000)AY.103:1.00000,AY.104:1.00000,AY.105:1.00000,AY.106:1.00000,AY.107:1.00000,AY.108:1.00000,AY.109:1.00000,AY.110:1.00000,AY.111:1.00000,(AY.112.1:1.00000,AY.112.2:1.00000,AY.112.3:1.00000)AY.112:1.00000,AY.113:1.00000,AY.114:1.00000,(AY.116.1:1.00000)AY.116:1.00000,AY.117:1.00000,AY.118:1.00000,(AY.119.1:1.00000,AY.119.2:1.00000)AY.119:1.00000,(AY.120.1:1.00000,(AY.120.2.1:1.00000)AY.120.2:1.00000)AY.120:1.00000,(AY.121.1:1.00000)AY.121:1.00000,(AY.122.1:1.00000,AY.122.2:1.00000,AY.122.3:1.00000,AY.122.4:1.00000,AY.122.5:1.00000,AY.122.6:1.00000)AY.122:1.00000,(AY.123.1:1.00000)AY.123:1.00000,((AY.124.1.1:1.00000)AY.124.1:1.00000)AY.124:1.00000,(AY.125.1:1.00000)AY.125:1.00000,AY.126:1.00000,(AY.127.1:1.00000,AY.127.2:1.00000,AY.127.3:1.00000)AY.127:1.00000,AY.128:1.00000,AY.129:1.00000,AY.130:1.00000,AY.131:1.00000,AY.132:1.00000,AY.133:1.00000,AY.134:1.00000)B.1.617.2:1.00000,B.1.617.3:1.00000)B.1.617:1.00000,B.1.618:1.00000,(B.1.619.1:1.00000)B.1.619:1.00000,B.1.620:1.00000,((BB.2:1.00000)B.1.621.1:1.00000,B.1.621.2:1.00000)B.1.621:1.00000,B.1.622:1.00000,B.1.623:1.00000,B.1.625:1.00000,B.1.626:1.00000,B.1.627:1.00000,B.1.629:1.00000,B.1.630:1.00000,B.1.631:1.00000,B.1.632:1.00000,B.1.633:1.00000,B.1.634:1.00000,B.1.635:1.00000,B.1.636:1.00000,(B.1.637.1:1.00000)B.1.637:1.00000,B.1.638:1.00000,B.1.639:1.00000,(B.1.640.1:1.00000,B.1.640.2:1.00000)B.1.640:1.00000,B.1.641:1.00000,(B.1.9.1:1.00000,B.1.9.2:1.00000,B.1.9.3:1.00000,B.1.9.4:1.00000,B.1.9.5:1.00000)B.1.9:1.00000)B.1:1.00000,(B.3.1:1.00000)B.3:1.00000,(B.4.1:1.00000,B.4.2:1.00000,B.4.4:1.00000,B.4.5:1.00000,B.4.6:1.00000,B.4.7:1.00000,B.4.8:1.00000)B.4:1.00000,B.5:1.00000,(B.6.1:1.00000,B.6.2:1.00000,B.6.3:1.00000,B.6.4:1.00000,B.6.5:1.00000,B.6.6:1.00000,B.6.8:1.00000)B.6:1.00000,B.10:1.00000,B.11:1.00000,B.12:1.00000,B.13:1.00000,B.15:1.00000,B.18:1.00000,B.19:1.00000,B.20:1.00000,B.23:1.00000,B.26:1.00000,B.27:1.00000,B.28:1.00000,B.29:1.00000,B.30:1.00000,B.31:1.00000,B.32:1.00000,B.33:1.00000,B.34:1.00000,B.35:1.00000,B.36:1.00000,B.37:1.00000,B.38:1.00000,B.39:1.00000,B.40:1.00000,B.41:1.00000,B.42:1.00000,B.43:1.00000,B.44:1.00000,B.45:1.00000,B.46:1.00000,B.47:1.00000,B.49:1.00000,B.50:1.00000,B.51:1.00000,B.52:1.00000,B.53:1.00000,B.55:1.00000,B.56:1.00000,B.57:1.00000,B.58:1.00000,B.60:1.00000,B.61:1.00000)B:1.00000)A:1.00000)MRCA:1.00000;
diff --git a/sc2rf/mapping.csv b/sc2rf/mapping.csv
index 9e4bc4e..71fc428 100644
--- a/sc2rf/mapping.csv
+++ b/sc2rf/mapping.csv
@@ -45,13 +45,14 @@ NextstrainClade,PangoLineage,Letter,WhoLabel,Other,WhoClass,Query
,AY.100,δ,Delta,,VOC,
,AY.25.1,δ,Delta,,VOC,
,AY.25,δ,Delta,,VOC,
-,BA.2.3,,,,,
-,BA.2.3.17,,,,,
-,BA.2.3.20,,,,,
-,BA.2.10,,,,,
-,BA.2.10.1,,,,,
+,BA.2.3,ο,Omicron,,VOC,
+,BA.2.3.17,ο,Omicron,,VOC,
+,BA.2.3.20,ο,Omicron,,VOC,
+,BA.2.10,ο,Omicron,,VOC,
+,BA.2.10.1,ο,Omicron,,VOC,
,BA.2.12.1,ο,Omicron,,VOC,
-,BA.2.75.3,,,,,
+,BA.2.75.3,ο,Omicron,,VOC,
+,BA.2.76,ο,Omicron,,VOC,
,BA.3,ο,Omicron,,VOC,
,BA.5.1,ο,Omicron,,VOC,
,BA.5.2,ο,Omicron,,VOC,
@@ -60,6 +61,7 @@ NextstrainClade,PangoLineage,Letter,WhoLabel,Other,WhoClass,Query
,B.1.631,,,,,
,B.1.438.1,,,,,
,B.1.634,,,,,
-,BJ.1,,,,,
-,BM.1,,,,,
-,BM.1.1,,,,,
+,BJ.1,ο,Omicron,,VOC,
+,BM.1,ο,Omicron,,VOC,
+,BM.1.1,ο,Omicron,,VOC,
+,CJ.1,ο,Omicron,,VOC,
diff --git a/sc2rf/postprocess.py b/sc2rf/postprocess.py
index 928112e..4ca75fa 100644
--- a/sc2rf/postprocess.py
+++ b/sc2rf/postprocess.py
@@ -10,15 +10,45 @@
from Bio import Phylo
NO_DATA_CHAR = "NA"
-LAPIS_URL_BASE = (
+
+LAPIS_LINEAGE_COL = "pangoLineage"
+LAPIS_OPEN_BASE = (
"https://lapis.cov-spectrum.org/open/v1/sample/aggregated"
- + "?fields=pangoLineage&nucMutations="
+ + "?fields={lineage_col}".format(lineage_col=LAPIS_LINEAGE_COL)
+ + "&nucMutations={mutations}"
+)
+
+LAPIS_GISAID_BASE = (
+ "https://lapis.cov-spectrum.org/gisaid/v1/sample/aggregated"
+ + "?accessKey={access_key}"
+ + "&fields={lineage_col}".format(lineage_col=LAPIS_LINEAGE_COL)
+ + "&nucMutations={mutations}"
)
+
LINEAGE_PROP_THRESHOLD = 0.01
LAPIS_SLEEP_TIME = 0
# Consider a breakpoint match if within 50 base pairs
BREAKPOINT_APPROX_BP = 50
+DATAFRAME_COLS = [
+ "sc2rf_status",
+ "sc2rf_details",
+ "sc2rf_lineage",
+ "sc2rf_clades_filter",
+ "sc2rf_regions_filter",
+ "sc2rf_regions_length",
+ "sc2rf_breakpoints_filter",
+ "sc2rf_num_breakpoints_filter",
+ "sc2rf_breakpoints_motif",
+ "sc2rf_unique_subs_filter",
+ "sc2rf_alleles_filter",
+ "sc2rf_intermission_allele_ratio",
+ "cov-spectrum_parents",
+ "cov-spectrum_parents_confidence",
+ "cov-spectrum_parents_subs",
+ "sc2rf_parents_conflict",
+]
+
def create_logger(logfile=None):
# create logger
@@ -160,6 +190,11 @@ def reverse_iter_collapse(
required=False,
default=False,
)
+@click.option(
+ "--gisaid-access-key",
+ help="The accessKey to query GISAID data with LAPIS",
+ required=False,
+)
@click.option(
"--issues",
help="Issues TSV metadata from pango-designation",
@@ -213,6 +248,7 @@ def main(
metadata,
dup_method,
lapis,
+ gisaid_access_key,
):
"""Detect recombinant seqences from sc2rf. Dependencies: pandas, click"""
@@ -224,7 +260,77 @@ def main(
logger = create_logger(logfile=log)
# -----------------------------------------------------------------------------
- # Import Dataframes
+ # Import Optional Data Files
+
+ # (Optional) issues.tsv of pango-designation issues
+ # lineage assignment by parent+breakpoint matching
+ if issues:
+
+ logger.info("Parsing issues: {}".format(issues))
+
+ breakpoint_col = "breakpoints_curated"
+ parents_col = "parents_curated"
+ breakpoint_df = pd.read_csv(issues, sep="\t")
+ breakpoint_df.fillna(NO_DATA_CHAR, inplace=True)
+ drop_rows = breakpoint_df[breakpoint_df[breakpoint_col] == NO_DATA_CHAR].index
+ breakpoint_df.drop(drop_rows, inplace=True)
+
+ # Convert CSV to lists
+ breakpoint_df[breakpoint_col] = [
+ bp.split(",") for bp in breakpoint_df[breakpoint_col]
+ ]
+ breakpoint_df[parents_col] = [p.split(",") for p in breakpoint_df[parents_col]]
+
+ # (Optional) motifs dataframe
+ if motifs:
+ logger.info("Parsing motifs: {}".format(motifs))
+ motifs_df = pd.read_csv(motifs, sep="\t")
+
+ # (Optional) nextclade tsv dataframe
+ if nextclade:
+ logger.info("Parsing nextclade: {}".format(nextclade))
+ nextclade_df = pd.read_csv(nextclade, sep="\t")
+ nextclade_df["seqName"] = [str(s) for s in nextclade_df["seqName"]]
+ nextclade_df.set_index("seqName", inplace=True)
+ nextclade_df.fillna(NO_DATA_CHAR, inplace=True)
+
+ if nextclade_auto_pass:
+ nextclade_auto_pass_lineages = nextclade_auto_pass.split(",")
+ # Identify samples to autopass
+ auto_pass_df = nextclade_df[
+ (nextclade_df["Nextclade_pango"] != NO_DATA_CHAR)
+ & (nextclade_df["Nextclade_pango"].isin(nextclade_auto_pass_lineages))
+ ]
+
+ # (Optional) nextclade tsv dataframe no-recomb dataset
+ if nextclade_no_recomb:
+ logger.info(
+ "Parsing nextclade no-recomb output: {}".format(nextclade_no_recomb)
+ )
+ nextclade_no_recomb_df = pd.read_csv(nextclade_no_recomb, sep="\t")
+ nextclade_no_recomb_df["seqName"] = [
+ str(s) for s in nextclade_no_recomb_df["seqName"]
+ ]
+ nextclade_no_recomb_df.set_index("seqName", inplace=True)
+ nextclade_no_recomb_df.fillna(NO_DATA_CHAR, inplace=True)
+
+ # (Optional) metadata tsv dataframe to find negatives missing from sc2rf
+ if metadata:
+ logger.info("Parsing metadata tsv: {}".format(metadata))
+ metadata_df = pd.read_csv(metadata, sep="\t")
+ metadata_df["strain"] = [str(s) for s in metadata_df["strain"]]
+ metadata_df.fillna(NO_DATA_CHAR, inplace=True)
+
+ # (Optional) phylogenetic tree of pangolineage lineages
+ if lineage_tree:
+ logger.info("Parsing lineage tree: {}".format(lineage_tree))
+ tree = Phylo.read(lineage_tree, "newick")
+
+ # -----------------------------------------------------------------------------
+ # Import Dataframes of Potential Positive Recombinants
+
+ # note: Add the end of this section, only positives and false positives will
+ # be in the dataframe.
# sc2rf csv output (required)
df = pd.DataFrame()
@@ -236,7 +342,7 @@ def main(
logger.info("Parsing csv: {}".format(csv_file))
try:
- temp_df = pd.read_csv(csv_file, sep=",", index_col=0)
+ temp_df = pd.read_csv(csv_file, sep=",")
except pd.errors.EmptyDataError:
logger.warning("No records in csv: {}".format(csv_file))
temp_df = pd.DataFrame()
@@ -248,6 +354,10 @@ def main(
value=os.path.basename(csv_file),
)
+ # Convert to str type
+ temp_df["sample"] = [str(s) for s in temp_df["sample"]]
+ temp_df.set_index("sample", inplace=True)
+
# Add column to hold original strain name before marking duplicates
temp_df.insert(
loc=len(temp_df.columns),
@@ -259,18 +369,7 @@ def main(
if len(df) == 0:
df = temp_df
else:
- # -----------------------------------------------------------------
- # Option 1: Override
- # Identify new strains in to override previous csv
- # override_strains = []
- # for strain in temp_df.index:
- # if strain in df.index:
- # override_strains.append(strain)
-
- # # Remove the override strains from the previous dataframe
- # df = df[~df.index.isin(override_strains)]
-
- # Option 2: Keep all dups for now, only retain best results at end
+ # Keep all dups for now, only retain best results at end
for strain in temp_df.index:
if strain in list(df["strain"]):
if strain not in duplicate_strains:
@@ -288,90 +387,105 @@ def main(
# Combine primary and secondary data frames
df = pd.concat([df, temp_df])
- # Remove temporary strain column
- df.drop(columns="strain", inplace=True)
-
df.fillna("", inplace=True)
- # Initialize dataframe columns to NA
- df["sc2rf_status"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_details"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_lineage"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_clades_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_regions_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_regions_length"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_breakpoints_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_num_breakpoints_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_breakpoints_motif"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_unique_subs_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_alleles_filter"] = [NO_DATA_CHAR] * len(df)
- df["sc2rf_intermission_allele_ratio"] = [NO_DATA_CHAR] * len(df)
- df["cov-spectrum_parents"] = [NO_DATA_CHAR] * len(df)
- df["cov-spectrum_parents_confidence"] = [NO_DATA_CHAR] * len(df)
- df["cov-spectrum_parents_subs"] = [NO_DATA_CHAR] * len(df)
-
- # if using issues.tsv of pango-designation issues (optional)
- # does lineage assignment by parent+breakpoint matching
- if issues:
+ # -------------------------------------------------------------------------
+ # Initialize new stat columns to NA
- logger.info("Parsing issues: {}".format(issues))
+ # note: Add the end of this section, only positives and false positives will
+ # be in the sc2rf_details_dict
- breakpoint_col = "breakpoints_curated"
- parents_col = "parents_curated"
- breakpoint_df = pd.read_csv(issues, sep="\t")
- breakpoint_df.fillna(NO_DATA_CHAR, inplace=True)
- drop_rows = breakpoint_df[breakpoint_df[breakpoint_col] == NO_DATA_CHAR].index
- breakpoint_df.drop(drop_rows, inplace=True)
+ for col in DATAFRAME_COLS:
+ df[col] = [NO_DATA_CHAR] * len(df)
- # Convert CSV to lists
- breakpoint_df[breakpoint_col] = [
- bp.split(",") for bp in breakpoint_df[breakpoint_col]
- ]
- breakpoint_df[parents_col] = [p.split(",") for p in breakpoint_df[parents_col]]
+ # Initialize a dictionary of sc2rf details and false positives
+ # key: strain, value: list of reasons
+ false_positives_dict = {}
+ sc2rf_details_dict = {strain: [] for strain in df.index}
- # (Optional) motifs dataframe
- if motifs:
- logger.info("Parsing motifs: {}".format(motifs))
- motifs_df = pd.read_csv(motifs, sep="\t")
+ # -------------------------------------------------------------------------
+ # Add in the Negatives (if metadata was specified)
+ # Avoiding the Bio module, I just need names not sequences
- # (Optional) nextclade tsv dataframe
- if nextclade:
- logger.info("Parsing nextclade: {}".format(nextclade))
- nextclade_df = pd.read_csv(nextclade, sep="\t", index_col=0)
- nextclade_df.fillna(NO_DATA_CHAR, inplace=True)
+ if metadata:
- if nextclade_auto_pass:
- nextclade_auto_pass_lineages = nextclade_auto_pass.split(",")
+ logger.info("Reporting non-recombinants in metadata as negatives")
+ for strain in list(metadata_df["strain"]):
+ # Ignore this strain if it's already in dataframe (it's a recombinant)
+ if strain in list(df["strain"]):
+ continue
+ # Otherwise add it, with no data as default
+ df.loc[strain] = NO_DATA_CHAR
+ df.at[strain, "strain"] = strain
+ df.at[strain, "sc2rf_status"] = "negative"
+ sc2rf_details_dict[strain] = []
+
+ # ---------------------------------------------------------------------
+ # Auto-pass lineages from nextclade assignment, that were also detected by sc2rf
+
+ if nextclade and nextclade_auto_pass:
- # (Optional) nextclade tsv dataframe no-recomb dataset
- if nextclade_no_recomb:
logger.info(
- "Parsing nextclade no-recomb output: {}".format(nextclade_no_recomb)
+ "Auto-passing lineages: {}".format(",".join(nextclade_auto_pass_lineages))
)
- nextclade_no_recomb_df = pd.read_csv(nextclade_no_recomb, sep="\t", index_col=0)
- nextclade_no_recomb_df.fillna(NO_DATA_CHAR, inplace=True)
- # (Optional) metadata tsv dataframe to find negatives missing from sc2rf
- if metadata:
- logger.info("Parsing metadata tsv: {}".format(metadata))
- metadata_df = pd.read_csv(metadata, sep="\t")
- metadata_df.fillna(NO_DATA_CHAR, inplace=True)
+ for rec in auto_pass_df.iterrows():
- # (Optional) phylogenetic tree of pangolineage lineages
- if lineage_tree:
- logger.info("Parsing lineage tree: {}".format(lineage_tree))
- tree = Phylo.read(lineage_tree, "newick")
+ # Note the strain is the true strain, not _dup suffix
+ strain = rec[0]
+
+ lineage = auto_pass_df.loc[strain]["Nextclade_pango"]
+ details = "nextclade-auto-pass {}".format(lineage)
+
+ # Case 1 : In df with NO DUPS, set the status to positive, update details
+ if strain in list(df.index):
+
+ sc2rf_details_dict[strain].append(details)
+ df.at[strain, "sc2rf_status"] = "positive"
+ df.at[strain, "sc2rf_details"] = ";".join(sc2rf_details_dict[strain])
+
+ # Case 2: In df WITH DUPS, set the dups status to positive, update details
+ elif strain in list(df["strain"]):
+
+ dup_strains = list(df[df["strain"] == strain].index)
+ for dup in dup_strains:
+
+ sc2rf_details_dict[dup].append(details)
+ df.at[dup, "sc2rf_status"] = "positive"
+ df.at[dup, "sc2rf_details"] = ";".join(sc2rf_details_dict[dup])
+
+ # Case 3: If it's not in the dataframe add it
+ else:
+ sc2rf_details_dict[strain] = [details]
- # Initialize a dictionary of false_positive strains
- # key: strain, value: reason
- false_positives = {}
+ # new row
+ row_dict = {col: [NO_DATA_CHAR] for col in df.columns}
+ row_dict["strain"] = strain
+ row_dict["sc2rf_status"] = "positive"
+ row_dict["sc2rf_details"] = ";".join(sc2rf_details_dict[strain])
+ row_df = pd.DataFrame(row_dict).set_index("strain")
+ df = pd.concat([df, row_df], ignore_index=False)
+
+ # -------------------------------------------------------------------------
+ # Begin Post-Processing
logger.info("Post-processing table")
+ # Iterate through all positive and negative recombinantions
for rec in df.iterrows():
+ # Skip over negative recombinants
+ if rec[1]["sc2rf_status"] == "negative":
+ continue
+
strain = rec[0]
+ # Check if this strain was auto-passed
+ strain_auto_pass = False
+ for detail in sc2rf_details_dict[strain]:
+ if "auto-pass" in detail:
+ strain_auto_pass = True
+
regions_str = rec[1]["regions"]
regions_split = regions_str.split(",")
@@ -398,7 +512,15 @@ def main(
# ---------------------------------------------------------------------
# FIRST PASS
+ # If the region is NA, this is an auto-passed recombinant
+ # and sc2rf couldn't find any breakpoints, skip the rest of processing
+ # and continue on to next strain
+
+ if regions_split == [NO_DATA_CHAR]:
+ continue
+
for region in regions_split:
+
coords = region.split("|")[0]
clade = region.split("|")[1]
start_coord = int(coords.split(":")[0])
@@ -519,7 +641,10 @@ def main(
# -----------------------------------------------------------------
# Check if all the regions were collapsed
if len(regions_filter) < 2:
- false_positives[rec[0]] = "single parent"
+ sc2rf_details_dict[strain].append("single parent")
+ # if this is an auto-pass lineage, don't add to false positives
+ if not strain_auto_pass:
+ false_positives_dict[strain] = ""
# -----------------------------------------------------------------
# FOURTH PASS: BREAKPOINT DETECTION
@@ -549,10 +674,14 @@ def main(
# Check for too many breakpoints
if max_breakpoints != -1:
if num_breakpoints_filter > max_breakpoints:
- false_positives[strain] = "{} breakpoints > {} max breakpoints".format(
+ details = "{} breakpoints > {} max breakpoints".format(
num_breakpoints_filter,
max_breakpoints,
)
+ sc2rf_details_dict[strain].append(details)
+ # if this is an auto-pass lineage, don't add to false positives
+ if not strain_auto_pass:
+ false_positives_dict[strain] = ""
# Identify the new filtered clades
clades_filter = [regions_filter[s]["clade"] for s in regions_filter]
@@ -560,75 +689,88 @@ def main(
num_parents = len(set(clades_filter))
if max_parents != -1:
if num_parents > max_parents:
- false_positives[strain] = "{} parents > {}".format(
- num_parents, max_parents
- )
+ details = "{} parents > {}".format(num_parents, max_parents)
+ sc2rf_details_dict[strain].append(details)
+ # if this is an auto-pass lineage, don't add to false positives
+ if not strain_auto_pass:
+ false_positives_dict[strain] = ""
# ---------------------------------------------------------------------
# Intermission/Minor Parent Allele Ratio
# ie. alleles that conflict with the parental region
+ # be lenient if there were more parents initially reported by sc2rf (ex. >2)
+ # because this wil lead to large numbers of unresolved intermissions
- for start_coord in regions_filter:
- end_coord = regions_filter[start_coord]["end"]
- clade = regions_filter[start_coord]["clade"]
+ original_parents = rec[1]["examples"]
+ num_original_parents = len(original_parents.split(","))
- for allele in alleles_split:
+ if num_original_parents > num_parents:
+ intermission_allele_ratio = "NA"
+ else:
+ for start_coord in regions_filter:
+ end_coord = regions_filter[start_coord]["end"]
+ clade = regions_filter[start_coord]["clade"]
- allele_coord = int(allele.split("|")[0])
- allele_clade = allele.split("|")[1]
- allele_nuc = allele.split("|")[2]
+ for allele in alleles_split:
- # Skip if this allele is not found in the current region
- if allele_coord < start_coord or allele_coord > end_coord:
- continue
+ allele_coord = int(allele.split("|")[0])
+ allele_clade = allele.split("|")[1]
+ allele_nuc = allele.split("|")[2]
+
+ # Skip if this allele is not found in the current region
+ if allele_coord < start_coord or allele_coord > end_coord:
+ continue
+
+ # Skip missing data
+ if allele_nuc == "N":
+ continue
+
+ # Check if this allele's origins conflicts with the parental region
+ if allele_clade != clade:
+ intermission_alleles.append(allele)
+ else:
+ if clade not in alleles_by_parent:
+ alleles_by_parent[clade] = []
+ alleles_by_parent[clade].append(allele)
+ # Add in alleles that were not assigned to any region
+ for allele in alleles_split:
+ allele_nuc = allele.split("|")[2]
# Skip missing data
if allele_nuc == "N":
continue
- # Check if this allele's origins conflicts with the parental region
- if allele_clade != clade:
+ allele_coord = int(allele.split("|")[0])
+ allele_in_region = False
+ for start_coord in regions_filter:
+ end_coord = regions_filter[start_coord]["end"]
+ if allele_coord >= start_coord and allele_coord <= end_coord:
+ allele_in_region = True
+
+ # Alleles not assigned to any region are counted as intermissions
+ if not allele_in_region:
intermission_alleles.append(allele)
- else:
- if clade not in alleles_by_parent:
- alleles_by_parent[clade] = []
- alleles_by_parent[clade].append(allele)
-
- # Add in alleles that were not assigned to any region
- for allele in alleles_split:
- allele_nuc = allele.split("|")[2]
- # Skip missing data
- if allele_nuc == "N":
- continue
- allele_coord = int(allele.split("|")[0])
- allele_in_region = False
- for start_coord in regions_filter:
- end_coord = regions_filter[start_coord]["end"]
- if allele_coord >= start_coord and allele_coord <= end_coord:
- allele_in_region = True
-
- # Alleles not assigned to any region are counted as intermissions
- if not allele_in_region:
- intermission_alleles.append(allele)
-
- # Identify the "minor" parent (least number of alleles)
- # minor_parent = None
- minor_num_alleles = len(alleles_split)
+ # Identify the "minor" parent (least number of alleles)
+ # minor_parent = None
+ minor_num_alleles = len(alleles_split)
- for parent in alleles_by_parent:
- num_alleles = len(alleles_by_parent[parent])
- if num_alleles <= minor_num_alleles:
- # minor_parent = parent
- minor_num_alleles = num_alleles
+ for parent in alleles_by_parent:
+ num_alleles = len(alleles_by_parent[parent])
+ if num_alleles <= minor_num_alleles:
+ # minor_parent = parent
+ minor_num_alleles = num_alleles
- intermission_allele_ratio = len(intermission_alleles) / minor_num_alleles
+ intermission_allele_ratio = len(intermission_alleles) / minor_num_alleles
- # When the ratio is above 1, that means there are more intermission than
- # minor parent alleles. But don't override the false_positive status
- # if this strain was already flagged as a false_positive previously
- if intermission_allele_ratio >= 1 and rec[0] not in false_positives:
- false_positives[rec[0]] = "intermission_allele_ratio >= 1"
+ # When the ratio is above 1, that means there are more intermission than
+ # minor parent alleles. But don't override the false_positive status
+ # if this strain was already flagged as a false_positive previously
+ if intermission_allele_ratio >= 1:
+ sc2rf_details_dict[strain].append("intermission_allele_ratio >= 1")
+ # if this is an auto-pass lineage, don't add to false positives
+ if not strain_auto_pass:
+ false_positives_dict[strain] = ""
# --------------------------------------------------------------------------
# Extract the lengths of each region
@@ -748,9 +890,10 @@ def main(
# Override the linaege call if one breakpoint had no motif
if False in breakpoints_motifs:
- df.at[strain, "sc2rf_lineage"] = "false_positive"
- df.at[strain, "sc2rf_status"] = "false_positive"
- false_positives[strain] = "missing breakpoint motif"
+ sc2rf_details_dict[strain].append("missing breakpoint motif")
+ # if this is an auto-pass lineage, don't add to false positives
+ if not strain_auto_pass:
+ false_positives_dict[strain] = ""
df.at[strain, "sc2rf_clades_filter"] = ",".join(clades_filter)
df.at[strain, "sc2rf_regions_filter"] = ",".join(regions_filter)
@@ -760,13 +903,19 @@ def main(
df.at[strain, "sc2rf_unique_subs_filter"] = ",".join(unique_subs_filter)
df.at[strain, "sc2rf_alleles_filter"] = ",".join(alleles_filter)
df.at[strain, "sc2rf_intermission_allele_ratio"] = intermission_allele_ratio
- if strain in false_positives:
- df.at[strain, "sc2rf_status"] = "false_positive"
- df.at[strain, "sc2rf_details"] = false_positives[strain]
- df.at[strain, "sc2rf_breakpoints_filter"] = NO_DATA_CHAR
- else:
+
+ if strain not in false_positives_dict:
df.at[strain, "sc2rf_status"] = "positive"
- df.at[strain, "sc2rf_details"] = "recombination detected"
+ # A sample can be positive with no breakpoints, if auto-pass
+ if len(breakpoints_filter) != 0:
+ sc2rf_details_dict[strain] = ["recombination detected"]
+ # For auto-pass negatives, update the csv_file
+ else:
+ df.at[strain, "csv_file"] = NO_DATA_CHAR
+ else:
+ df.at[strain, "sc2rf_status"] = "false_positive"
+
+ df.at[strain, "sc2rf_details"] = ";".join(sc2rf_details_dict[strain])
# ---------------------------------------------------------------------
# Resolve strains with duplicate results
@@ -774,6 +923,11 @@ def main(
logger.info("Reconciling duplicate results with method: {}".format(dup_method))
for strain in duplicate_strains:
+ # Check if this strain was auto-passed
+ strain_auto_pass = True if strain in auto_pass_df.index else False
+ strain_df = df[df["strain"] == strain]
+ strain_bp = list(set(strain_df["sc2rf_breakpoints_filter"]))
+
num_dups = duplicate_strains[strain]
# Which duplicates should we keep (1), which should we remove (many)
keep_dups = []
@@ -794,24 +948,34 @@ def main(
keep_dups.append(keep_strain)
remove_dups.remove(keep_strain)
- # Case 2. Multiple keeps found, use dup_method
+ # Case 2. Multiple keeps found, but it's an auto-pass and they're all negative
+ # Just take the first csv
+ elif strain_auto_pass and strain_bp == [""]:
+ remove_dups += keep_dups[1:]
+ keep_dups = [keep_dups[0]]
+
+ # Case 3. Multiple keeps found, use dup_method
elif len(keep_dups) > 1:
# First try to match to a published lineage
- lineage_strain = None
+ # Key = dup_strain, value = csv of lineages
+ lineage_strains = {}
for dup_strain in keep_dups:
dup_lineage = df["sc2rf_lineage"][dup_strain]
if dup_lineage != NO_DATA_CHAR:
- lineage_strain = dup_strain
+ lineage_strains[dup_strain] = dup_lineage
- # Check if a match was found
- if lineage_strain:
+ lineage_strains_matches = list(set(lineage_strains.values()))
+
+ # Check if a match was found, but not more than 1 candidate lineage
+ if len(lineage_strains) > 0 and len(lineage_strains_matches) < 2:
+ keep_strain = list(lineage_strains)[0]
# Remove all strains except the min one
remove_dups = keep_dups + remove_dups
- remove_dups.remove(lineage_strain)
- keep_dups = [lineage_strain]
+ remove_dups.remove(keep_strain)
+ keep_dups = [keep_strain]
# Otherwise, use a duplicate resolving method
elif dup_method == "first":
@@ -848,6 +1012,9 @@ def main(
# '8394:12879,13758:22000'
bp = df["sc2rf_breakpoints_filter"][dup_strain]
# ['8394:12879','13758:22000']
+ # Skip if this is an auto-pass lineage with no breakpoints
+ if bp == "":
+ continue
bp = bp.split(",")
# [4485, 8242]
bp = [int(c.split(":")[1]) - int(c.split(":")[0]) for c in bp]
@@ -866,7 +1033,11 @@ def main(
remove_dups.remove(min_uncertainty_strain)
keep_dups = [min_uncertainty_strain]
- keep_csv_file = df["csv_file"][keep_dups[0]]
+ # If this is an auto-pass negative, indicate no csvfile was used
+ if strain_bp == [""]:
+ keep_csv_file = NO_DATA_CHAR
+ else:
+ keep_csv_file = df["csv_file"][keep_dups[0]]
logger.info(
"Reconciling duplicate results for {}, retaining output from: {}".format(
strain, keep_csv_file
@@ -877,17 +1048,21 @@ def main(
# Drop the rejected duplicates
df.drop(labels=remove_dups, inplace=True)
+ # ---------------------------------------------------------------------
# Fix up duplicate strain names in false_positives
+
false_positives_filter = {}
- for strain in false_positives:
+ for strain in false_positives_dict:
+
strain_orig = strain.split("_dup")[0]
status = df["sc2rf_status"][strain_orig]
# If the original strain isn't positive
# All duplicates were false positives and should be removed
if status != "positive":
- false_positives_filter[strain_orig] = false_positives[strain]
+ false_positives_filter[strain_orig] = false_positives_dict[strain]
+ sc2rf_details_dict[strain_orig] = sc2rf_details_dict[strain]
- false_positives = false_positives_filter
+ false_positives_dict = false_positives_filter
# ---------------------------------------------------------------------
# Identify parent lineages by querying cov-spectrum mutations
@@ -903,6 +1078,14 @@ def main(
"Identifying parent lineages based on nextclade no-recomb substitutions"
)
+ # Check if we're using open data or GISAID
+ if gisaid_access_key:
+ lapis_url_base = LAPIS_GISAID_BASE.format(
+ access_key=gisaid_access_key, mutations="{mutations}"
+ )
+ else:
+ lapis_url_base = LAPIS_OPEN_BASE
+
positive_df = df[df["sc2rf_status"] == "positive"]
total_positives = len(positive_df)
progress_i = 0
@@ -913,6 +1096,14 @@ def main(
for rec in positive_df.iterrows():
strain = rec[0]
+ strain_bp = rec[1]["sc2rf_breakpoints_filter"]
+
+ # If this was an auto-pass negative strain, no regions for us to query
+ if strain_bp == NO_DATA_CHAR:
+ continue
+
+ parent_clades = rec[1]["sc2rf_clades_filter"].split(",")
+
progress_i += 1
logger.info("{} / {}: {}".format(progress_i, total_positives, strain))
@@ -940,7 +1131,15 @@ def main(
substitutions.remove(private)
# Split mutations by region
- for region in regions_filter:
+ for region, parent_clade in zip(regions_filter, parent_clades):
+
+ # If the parental clade is a recombinant, we'll allow the covlineages
+ # to be recombinants
+ parent_clade_is_recombinant = False
+ for label in parent_clade.split("/"):
+ if label.startswith("X"):
+ parent_clade_is_recombinant = True
+
region_coords = region.split("|")[0]
region_start = int(region_coords.split(":")[0])
region_end = int(region_coords.split(":")[1])
@@ -960,7 +1159,7 @@ def main(
# Otherwise, query cov-spectrum for these subs
else:
query_subs_dict[region_subs_csv] = ""
- url = LAPIS_URL_BASE + region_subs_csv
+ url = lapis_url_base.format(mutations=region_subs_csv)
logger.info(
"Querying cov-spectrum for region {}".format(region_coords)
)
@@ -973,8 +1172,19 @@ def main(
# Have keys be counts
lineage_dict = {}
+
for rec in lineage_data:
- lineage = rec["pangoLineage"]
+ lineage = rec[LAPIS_LINEAGE_COL]
+
+ # Ignore None lineages
+ if not lineage:
+ continue
+
+ # If parent clade is not a recombinant, don't include
+ # recombinant lineages in final dict and count
+ if lineage.startswith("X") and not parent_clade_is_recombinant:
+ continue
+
count = rec["count"]
lineage_dict[count] = lineage
@@ -994,8 +1204,17 @@ def main(
max_prop = max_count / total_count
max_lineage = lineage_dict[max_count]
- # Don't want to report recombinants as parents yet
- while max_lineage.startswith("X") or max_lineage == "Unassigned":
+ # Don't want to report recombinants as parents
+ # UNLESS, the parental clade is a recombinant (ex. XBB)
+ while (
+ max_lineage is None
+ or max_lineage.startswith("X")
+ or max_lineage == "Unassigned"
+ ):
+
+ # Allow the max_lineage to be recombinant if clade is also
+ if max_lineage.startswith("X") and parent_clade_is_recombinant:
+ break
lineage_dict = {
count: lineage
for count, lineage in lineage_dict.items()
@@ -1058,90 +1277,84 @@ def main(
df.at[strain, "cov-spectrum_parents_subs"] = ";".join(parent_lineages_subs)
# ---------------------------------------------------------------------
- # Auto-pass lineages from nextclade assignment
-
- if nextclade and nextclade_auto_pass:
-
- logger.info(
- "Auto-passing lineages through sc2rf: {}".format(
- ",".join(nextclade_auto_pass_lineages)
- )
- )
-
- # Identify negative samples to auto-pass
- auto_pass_df = nextclade_df[
- (nextclade_df["Nextclade_pango"] != NO_DATA_CHAR)
- & (nextclade_df["Nextclade_pango"].isin(nextclade_auto_pass_lineages))
- ]
-
- # If already in the df, set the status to positive, update details
- for rec in df[df.index.isin(auto_pass_df.index)].iterrows():
- strain = rec[0]
- lineage = auto_pass_df.loc[strain]["Nextclade_pango"]
- sc2rf_details = rec[1]["sc2rf_details"].split(";")
- sc2rf_details.append("nextclade-auto-pass {}".format(lineage))
+ # Identify parent conflict
- df.at[strain, "sc2rf_status"] = "positive"
- df.at[strain, "sc2rf_details"] = ";".join(sc2rf_details)
+ if nextclade_no_recomb and lapis and lineage_tree:
- # Remove this strain from the list of false positives
- if strain in false_positives:
- del false_positives[strain]
+ logger.info("Identifying parental conflict between lineage and clade.")
- # Filter the auto pass df to remove the samples already in the df
- auto_pass_df = auto_pass_df[~auto_pass_df.index.isin(df.index)]
+ positive_df = df[df["sc2rf_status"] == "positive"]
- # Construct a sc2rf df with blank cols except status and details blank
- auto_pass_dict = {col: [NO_DATA_CHAR] * len(auto_pass_df) for col in df.columns}
- auto_pass_dict["sample"] = []
+ for rec in positive_df.iterrows():
- for i, rec in enumerate(auto_pass_df.iterrows()):
strain = rec[0]
- lineage = rec[1]["Nextclade_pango"]
- # Additionally note that this was negative first
- sc2rf_details = [
- "no recombination detected",
- "nextclade-auto-pass {}".format(lineage),
- ]
+ parent_clades = rec[1]["sc2rf_clades_filter"].split(",")
+ parent_lineages = rec[1]["cov-spectrum_parents"].split(",")
+ conflict = False
+ lineage_is_descendant = False
+ lineage_is_ancestor = False
+
+ # Clade format can be:
+ # Lineage (BA.2.3.20)
+ # WHO_LABEL/Nextstrain clade (Delta/21J)
+ # WHO_LABEL/Lineage/Nextstrain clade (Omicron/BA.1/21K)
+ # WHO_LABEL/Lineage/Nextstrain clade (Omicron/XBB/22F)
+ # Lineage/Nextstrain clade (B.1.177/20E)
+
+ for i, labels in enumerate(parent_clades):
+ parent = NO_DATA_CHAR
+ labels_split = labels.split("/")
+ for label in labels_split:
+ # Need to find the lineage which contains "."
+ if "." in label:
+ parent = label
+ # Or a recombinant, which doesn't have a "."
+ elif label.startswith("X"):
+ parent = label
+ parent_clades[i] = parent
+
+ for c, l in zip(parent_clades, parent_lineages):
+
+ # We can't compare to NA
+ if c == NO_DATA_CHAR or l == NO_DATA_CHAR:
+ continue
+ # Remove the asterisk indicating all descendants
+ l = l.replace("*", "")
+ # If they are the same, there is no conflict, continue
+ if c == l:
+ continue
+
+ # Check if parents_lineage is descendant of parents_clade
+ c_node = [c for c in tree.find_clades(c)][0]
+ l_node = [c for c in c_node.find_clades(l)]
+ if len(l_node) != 1:
+ lineage_is_descendant = True
- auto_pass_dict["sample"].append(strain)
- auto_pass_dict["sc2rf_status"][i] = "positive"
- auto_pass_dict["sc2rf_details"][i] = ";".join(sc2rf_details)
+ # Check if parents_lineage is ancestor of parents_clade
+ l_node = [c for c in tree.find_clades(l)][0]
+ c_node = [c for c in l_node.find_clades(c)]
+ if len(c_node) != 1:
+ lineage_is_ancestor = True
- auto_pass_df = pd.DataFrame(auto_pass_dict).set_index("sample")
+ if not lineage_is_descendant and not lineage_is_ancestor:
+ conflict = True
- # Append the auto pass df to the main results df
- df = pd.concat([df, auto_pass_df])
+ df.at[strain, "sc2rf_parents_conflict"] = conflict
# ---------------------------------------------------------------------
# Write exclude strains (false positives)
outpath_exclude = os.path.join(outdir, prefix + ".exclude.tsv")
logger.info("Writing strains to exclude: {}".format(outpath_exclude))
- if len(false_positives) > 0:
+ if len(false_positives_dict) > 0:
with open(outpath_exclude, "w") as outfile:
- for strain, reason in false_positives.items():
- outfile.write(strain + "\t" + reason + "\n")
+ for strain in false_positives_dict:
+ details = ";".join(sc2rf_details_dict[strain])
+ outfile.write(strain + "\t" + details + "\n")
else:
cmd = "touch {outpath}".format(outpath=outpath_exclude)
os.system(cmd)
- # -------------------------------------------------------------------------
- # Add in the Negatives (if alignment was specified)
- # Avoiding the Bio module, I just need names not sequence
-
- if metadata:
-
- logger.info("Reporting non-recombinants in metadata as negatives")
- for strain in list(metadata_df["strain"]):
- # Ignore this strain if it's already in dataframe (it's a recombinant)
- if strain in df.index:
- continue
- # Otherwise add it, with no data as default
- df.loc[strain] = NO_DATA_CHAR
- df.at[strain, "sc2rf_status"] = "negative"
- df.at[strain, "sc2rf_details"] = "no recombination detected"
-
# -------------------------------------------------------------------------
# write output table
@@ -1161,6 +1374,8 @@ def main(
inplace=True,
)
+ # Move the strain column to the beginning
+ df.drop(columns="strain", inplace=True)
df.insert(loc=0, column="strain", value=df.index)
df.rename(
{
@@ -1201,7 +1416,7 @@ def main(
for i, ansi_file in enumerate(ansi_split):
logger.info("Parsing ansi: {}".format(ansi_file))
- if len(false_positives) > 0:
+ if len(false_positives_dict) > 0:
cmd = (
"cut -f 1 "
+ "{exclude} | grep -v -f - {inpath} {operator} {outpath}".format(
@@ -1218,7 +1433,7 @@ def main(
outpath=outpath_ansi,
)
logger.info("Writing filtered ansi: {}".format(outpath_ansi))
- logger.info(cmd)
+ # logger.info(cmd)
os.system(cmd)
# -------------------------------------------------------------------------
diff --git a/sc2rf/sc2rf.py b/sc2rf/sc2rf.py
index 3617175..7838a71 100644
--- a/sc2rf/sc2rf.py
+++ b/sc2rf/sc2rf.py
@@ -247,10 +247,14 @@ def main():
help="Path to write results in CSV format.",
)
parser.add_argument(
- "--ignore-shared-subs",
+ "--ignore-shared",
action="store_true",
help="Ignore substitutions that are shared between all parents.",
)
+ parser.add_argument(
+ "--gisaid-access-key",
+ help="covSPECTRUM accessKey for GISAID data.",
+ )
sc2rf_dir = os.path.dirname(os.path.realpath(__file__))
@@ -462,8 +466,12 @@ def rebuild_examples():
continue
print(f"Fetching data for {query}")
- url = f"https://lapis.cov-spectrum.org/open/v1/sample/nuc-mutations{query}&minProportion=0.05"
+ if args.gisaid_access_key == None:
+ url = f"https://lapis.cov-spectrum.org/open/v1/sample/nuc-mutations{query}&minProportion=0.05"
+ else:
+ url = f"https://lapis.cov-spectrum.org/gisaid/v1/sample/nuc-mutations{query}&minProportion=0.05&accessKey={args.gisaid_access_key}"
print(f"Url is {url}")
+
r = requests.get(url)
result = r.json()
if len(result["errors"]):
@@ -843,7 +851,7 @@ def show_matches(examples, samples, writer):
# Add in the private mutations
coords = coords.union(private_coords)
- if args.ignore_shared_subs:
+ if args.ignore_shared:
filter_coords = set()
for coord in coords:
parents_matches = 0
@@ -861,7 +869,7 @@ def show_matches(examples, samples, writer):
elif parents_matches <= 1:
filter_coords.add(coord)
# Check if we're showing substitutions found in multiple parents
- elif parents_matches > 1 and not args.ignore_shared_subs:
+ elif parents_matches > 1 and not args.ignore_shared:
filter_coords.add(coord)
coords = filter_coords
@@ -1088,7 +1096,7 @@ def show_matches(examples, samples, writer):
# all examples match
else:
- if args.ignore_shared_subs:
+ if args.ignore_shared:
continue
else:
alleles.append(
diff --git a/sc2rf/virus_properties.json b/sc2rf/virus_properties.json
index 7abf3ae..52940ed 100644
--- a/sc2rf/virus_properties.json
+++ b/sc2rf/virus_properties.json
@@ -11,230 +11,335 @@
"WhoClass": "",
"Query": "",
"mutations": [
- {
- "mutation": "C241T",
- "proportion": 0.9953517331701609,
- "count": 92720
- },
{
"mutation": "C3037T",
- "proportion": 0.9946580872310073,
- "count": 98313
+ "proportion": 0.996133990919776,
+ "count": 345786
},
{
"mutation": "C14408T",
- "proportion": 0.9938559941889206,
- "count": 98512
+ "proportion": 0.9939042414884671,
+ "count": 346315
},
{
"mutation": "G22992A",
- "proportion": 0.17437301095941,
- "count": 16054
+ "proportion": 0.14110189247671764,
+ "count": 47211
},
{
"mutation": "A23403G",
- "proportion": 0.9940895726771114,
- "count": 98729
+ "proportion": 0.9967013062376581,
+ "count": 353818
},
{
"mutation": "C23604A",
- "proportion": 0.06279446105944529,
- "count": 6131
+ "proportion": 0.05617768251657679,
+ "count": 19605
},
{
"mutation": "T21765-",
- "proportion": 0.06484007069375913,
- "count": 6347
+ "proportion": 0.09255678863738871,
+ "count": 32218
},
{
"mutation": "A21766-",
- "proportion": 0.0649435695940899,
- "count": 6347
+ "proportion": 0.09278023974683472,
+ "count": 32221
},
{
"mutation": "C21767-",
- "proportion": 0.06523653491705919,
- "count": 6371
+ "proportion": 0.09384409886750304,
+ "count": 32599
},
{
"mutation": "A21768-",
- "proportion": 0.06516975877462423,
- "count": 6365
+ "proportion": 0.0937075300025338,
+ "count": 32545
},
{
"mutation": "T21769-",
- "proportion": 0.06508954414817997,
- "count": 6364
+ "proportion": 0.09325101434918953,
+ "count": 32383
},
{
"mutation": "G21770-",
- "proportion": 0.06495100546202157,
- "count": 6350
+ "proportion": 0.0924830496748305,
+ "count": 32082
},
{
- "mutation": "G25563T",
- "proportion": 0.34745945280521956,
- "count": 33870
+ "mutation": "C241T",
+ "proportion": 0.9965518369872755,
+ "count": 334962
},
{
- "mutation": "A20268G",
- "proportion": 0.404930346303332,
- "count": 37090
+ "mutation": "A22-",
+ "proportion": 0.051798736871488886,
+ "count": 2969
},
{
- "mutation": "C18877T",
- "proportion": 0.27752424998976793,
- "count": 27123
+ "mutation": "T54-",
+ "proportion": 0.3944454358463807,
+ "count": 125991
},
{
- "mutation": "C28854T",
- "proportion": 0.3789302137288892,
- "count": 36505
+ "mutation": "T49-",
+ "proportion": 0.0655120720416372,
+ "count": 12411
},
{
- "mutation": "C4543T",
- "proportion": 0.18108146726816224,
- "count": 17742
+ "mutation": "G28975T",
+ "proportion": 0.06787097079987084,
+ "count": 22911
},
{
- "mutation": "T24076C",
- "proportion": 0.0770926030373295,
- "count": 7381
+ "mutation": "A12-",
+ "proportion": 0.07920685497760938,
+ "count": 3679
},
{
- "mutation": "C22444T",
- "proportion": 0.055950306117888905,
- "count": 5008
+ "mutation": "C32-",
+ "proportion": 0.09022570076447033,
+ "count": 9914
},
{
- "mutation": "C15324T",
- "proportion": 0.0698578707428265,
- "count": 6773
+ "mutation": "G15766T",
+ "proportion": 0.1238260780070271,
+ "count": 43313
},
{
- "mutation": "C25710T",
- "proportion": 0.17652311142489305,
- "count": 17002
+ "mutation": "G25563T",
+ "proportion": 0.3613071194970019,
+ "count": 124066
},
{
- "mutation": "G5629T",
- "proportion": 0.15913881989522238,
- "count": 15249
+ "mutation": "C28869T",
+ "proportion": 0.053359970833123375,
+ "count": 18002
},
{
- "mutation": "C26735T",
- "proportion": 0.24670009623664083,
- "count": 24353
+ "mutation": "T25-",
+ "proportion": 0.05968941120038374,
+ "count": 3982
+ },
+ {
+ "mutation": "A30-",
+ "proportion": 0.21882162599433733,
+ "count": 21099
+ },
+ {
+ "mutation": "C29870A",
+ "proportion": 0.08840063265811689,
+ "count": 4639
},
{
"mutation": "T7767C",
- "proportion": 0.07391260059081185,
- "count": 7256
+ "proportion": 0.09698779575572883,
+ "count": 33322
+ },
+ {
+ "mutation": "C8047T",
+ "proportion": 0.09676078424627099,
+ "count": 33752
+ },
+ {
+ "mutation": "G12988T",
+ "proportion": 0.07631450197139321,
+ "count": 26517
+ },
+ {
+ "mutation": "G15598A",
+ "proportion": 0.07646612085680259,
+ "count": 26581
},
{
"mutation": "C17104T",
- "proportion": 0.07535561698919327,
- "count": 7496
+ "proportion": 0.09874826543725228,
+ "count": 34656
},
{
- "mutation": "G17019T",
- "proportion": 0.16943965561211477,
- "count": 16610
+ "mutation": "G18028T",
+ "proportion": 0.07631927124739188,
+ "count": 26592
},
{
- "mutation": "G13993T",
- "proportion": 0.17690059957736368,
- "count": 17496
+ "mutation": "A20268G",
+ "proportion": 0.3341213797190193,
+ "count": 108258
+ },
+ {
+ "mutation": "C22879A",
+ "proportion": 0.1182483501896149,
+ "count": 40442
+ },
+ {
+ "mutation": "T24910C",
+ "proportion": 0.07593826277908636,
+ "count": 26662
+ },
+ {
+ "mutation": "T26972C",
+ "proportion": 0.07660758624102369,
+ "count": 26318
+ },
+ {
+ "mutation": "C27800A",
+ "proportion": 0.08317839137875215,
+ "count": 26976
},
{
"mutation": "G29734C",
- "proportion": 0.0787536748138452,
- "count": 7340
+ "proportion": 0.09968136688986377,
+ "count": 32379
},
{
- "mutation": "G18028T",
- "proportion": 0.05468670969308273,
- "count": 5406
+ "mutation": "A38-",
+ "proportion": 0.18976500702476953,
+ "count": 29580
},
{
- "mutation": "G15766T",
- "proportion": 0.17536614355647778,
- "count": 17410
+ "mutation": "C28887T",
+ "proportion": 0.05647287359511586,
+ "count": 19129
},
{
- "mutation": "C29870A",
- "proportion": 0.06647309569407549,
- "count": 1076
+ "mutation": "C18877T",
+ "proportion": 0.2963970127624442,
+ "count": 102674
},
{
- "mutation": "C8047T",
- "proportion": 0.07355145867098865,
- "count": 7261
+ "mutation": "C37-",
+ "proportion": 0.05169425118134543,
+ "count": 6531
},
{
- "mutation": "G12988T",
- "proportion": 0.054568358954925796,
- "count": 5328
+ "mutation": "A6-",
+ "proportion": 0.06556889787183451,
+ "count": 2206
},
{
- "mutation": "G15598A",
- "proportion": 0.05480071532816708,
- "count": 5332
+ "mutation": "A39-",
+ "proportion": 0.05392555217291108,
+ "count": 8892
},
{
- "mutation": "C22879A",
- "proportion": 0.07570202394828865,
- "count": 7144
+ "mutation": "C4543T",
+ "proportion": 0.1260277380793955,
+ "count": 43563
},
{
- "mutation": "T24910C",
- "proportion": 0.05469915464942814,
- "count": 5390
+ "mutation": "T3-",
+ "proportion": 0.1540834618751824,
+ "count": 3696
},
{
- "mutation": "T26972C",
- "proportion": 0.0550900560325631,
- "count": 5319
+ "mutation": "T10-",
+ "proportion": 0.05285214218803924,
+ "count": 2144
},
{
- "mutation": "C27800A",
- "proportion": 0.058207280495436206,
- "count": 5395
+ "mutation": "C28854T",
+ "proportion": 0.3100398512614628,
+ "count": 104640
+ },
+ {
+ "mutation": "A4-",
+ "proportion": 0.20985553664965134,
+ "count": 6290
+ },
+ {
+ "mutation": "G17019T",
+ "proportion": 0.1223928074272632,
+ "count": 42555
+ },
+ {
+ "mutation": "A29-",
+ "proportion": 0.060806615675240094,
+ "count": 4603
+ },
+ {
+ "mutation": "G5629T",
+ "proportion": 0.12157310846579979,
+ "count": 42026
},
{
"mutation": "G9526T",
- "proportion": 0.1775691489899245,
- "count": 17571
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},
{
- "mutation": "A764G",
- "proportion": 0.09316770186335403,
- "count": 30
+ "mutation": "G22017A",
+ "proportion": 0.0735873850197109,
+ "count": 56
},
{
- "mutation": "C15183T",
- "proportion": 0.6210191082802548,
- "count": 195
+ "mutation": "C14790T",
+ "proportion": 0.1935483870967742,
+ "count": 150
}
],
- "name": "BM.1.1"
+ "name": "Omicron / CJ.1"
}
]
}
diff --git a/scripts/lineage_tree.py b/scripts/lineage_tree.py
index d8effd3..c1de9f0 100644
--- a/scripts/lineage_tree.py
+++ b/scripts/lineage_tree.py
@@ -2,94 +2,90 @@
import click
import os
-import json
-from functions import create_logger
from Bio import Phylo
from Bio.Phylo.BaseTree import Clade
+import requests
+from pango_aliasor.aliasor import Aliasor
+LINEAGES_URL = (
+ "https://raw.githubusercontent.com/cov-lineages/pango-designation/master/"
+ + "lineage_notes.txt"
+)
-def build_tree(tree_data, tree=None, parent=None):
- node_name = tree_data["name"]
+@click.command()
+@click.option("--output", help="Output newick phylogeny.", required=True)
+def main(output):
+ """Create a nomenclature tree of pango lineages."""
- # ----------------------------------------------------------------------
- # Node name parsing
+ # Create output directory if it doesn't exist
+ outdir = os.path.dirname(output)
+ if not os.path.exists(outdir) and outdir != "":
+ os.mkdir(outdir)
- # for internal nodes, use the pango lineage annot as the name
- if "NODE_" in node_name or "internal" in node_name:
- node_name = tree_data["node_attrs"]["Nextclade_pango"]["value"]
+ # -------------------------------------------------------------------------
+ # Download Latest designated lineages from pango-designation
- # Handle rec_parent as parent node of recombinants
- elif node_name == "rec_parent":
- node_name = "X"
+ print("Downloading list of lineages: {}".format(LINEAGES_URL))
+ r = requests.get(LINEAGES_URL)
+ lineage_text = r.text
- # Nodes with non-lineage names, we will use the pango lineage instead
- incorrect_node_names = ["21M", "22612T", "BA2754"]
+ # Convert the text table to list
+ lineages = []
+ for line in lineage_text.split("\n"):
+ if "Withdrawn" in line or line.startswith("Lineage"):
+ continue
- if node_name in incorrect_node_names:
- node_name = tree_data["node_attrs"]["Nextclade_pango"]["value"]
+ lineage = line.split("\t")[0]
+ if lineage == "":
+ continue
+ lineages.append(lineage)
- # Nodes with the wrong parent
- if node_name == "CK.2.1":
- parent = "CK.2"
+ # Initialize the aliasor, which will download the latest aliases
+ aliasor = Aliasor()
- # ----------------------------------------------------------------------
- # Clade construction
+ # -------------------------------------------------------------------------
+ # Construct Tree
- # Check if we're just starting (create a root)
- if not tree:
- node_name = "MRCA"
- tree = Clade(name=node_name, clades=[], branch_length=1)
+ print("Constructing tree.")
- # Otherwise, this is not the root and has a parent
- else:
- # Check if the node_name is already in the tree
- tree_clades = [c.name for c in tree.find_clades()]
- if node_name not in tree_clades:
- parent_clade = [c for c in tree.find_clades(parent)]
- # If we found a parent
- if len(parent_clade) == 1:
- parent_clade = parent_clade[0]
- clade = Clade(name=node_name, clades=[], branch_length=1)
- parent_clade.clades.append(clade)
+ # Create a tree with a root node "MRCA"
+ tree = Clade(name="MRCA", clades=[], branch_length=1)
+ # Add an "X" parent for recombinants
+ clade = Clade(name="X", clades=[], branch_length=1)
+ tree.clades.append(clade)
- # Recurse through children
- if "children" in tree_data.keys():
- for child in tree_data["children"]:
- tree = build_tree(child, tree, parent=node_name)
+ for lineage in lineages:
- return tree
+ # Identify the parent
+ lineage_uncompress = aliasor.uncompress(lineage)
+ parent_uncompress = ".".join(lineage_uncompress.split(".")[0:-1])
+ parent = aliasor.compress(parent_uncompress)
+ # Manual parents setting for A and B
+ if lineage == "A":
+ parent = "MRCA"
-@click.command()
-@click.option(
- "--tree-json", help="Auspice phylogeny JSON from nextclade dataset.", required=True
-)
-@click.option("--output", help="Output newick phylogeny.", required=True)
-@click.option("--log", help="Log file.", required=False)
-def main(tree_json, output, log):
- """Create a newick tree out of the nextclade dataset Auspice JSON"""
-
- # Check output directory
- outdir = os.path.dirname(output)
- if not os.path.exists(outdir) and outdir != "":
- os.mkdir(outdir)
+ elif lineage == "B":
+ parent = "A"
- # create logger
- logger = create_logger(log)
+ # Special handling for recombinants
+ elif lineage.startswith("X") and parent == "":
+ parent = "X"
- # read in Auspice JSON
- logger.info("INFO:\tReading JSON.")
- with open(tree_json) as infile:
- tree_data = json.load(infile)["tree"]
+ parent_clade = [c for c in tree.find_clades(parent)]
+ # If we found a parent, as long as the input list is formatted correctly
+ # this should always be true
+ if len(parent_clade) == 1:
+ parent_clade = parent_clade[0]
+ clade = Clade(name=lineage, clades=[], branch_length=1)
+ parent_clade.clades.append(clade)
- # construct the tree
- logger.info("INFO:\tConstructing tree.")
- tree = build_tree(tree_data)
+ # -------------------------------------------------------------------------
+ # Export
- # export
tree_outpath = os.path.join(output)
- logger.info("INFO:\tExporting tree: {}".format(tree_outpath))
+ print("Exporting tree: {}".format(tree_outpath))
Phylo.write(tree, tree_outpath, "newick")
diff --git a/scripts/linelist.py b/scripts/linelist.py
index cbaf474..cfff5ed 100755
--- a/scripts/linelist.py
+++ b/scripts/linelist.py
@@ -14,6 +14,12 @@
PIPELINE = "ncov-recombinant"
+# There are alternative lineage names for sequencing dropouts
+SC2RF_LINEAGE_ALT = {
+ "XBB_ARTICv4.1": "XBB",
+ "XAY_dropout": "XAY",
+}
+
# Select and rename columns from summary
LINELIST_COLS = {
"strain": "strain",
@@ -23,6 +29,7 @@
"Nextclade_clade": "clade_nextclade",
"sc2rf_parents": "parents_clade",
"cov-spectrum_parents": "parents_lineage",
+ "sc2rf_parents_conflict": "parents_conflict",
"cov-spectrum_parents_confidence": "parents_lineage_confidence",
"cov-spectrum_parents_subs": "parents_subs",
"sc2rf_breakpoints": "breakpoints",
@@ -166,6 +173,11 @@ def main(
lineages_sc2rf = rec[1]["lineage_sc2rf"].split(",")
status = rec[1]["status_sc2rf"]
+ # Rename sc2rf lineages for alts (ex. ARTIC XBB)
+ for i, l_sc2rf in enumerate(lineages_sc2rf):
+ if l_sc2rf in SC2RF_LINEAGE_ALT:
+ lineages_sc2rf[i] = SC2RF_LINEAGE_ALT[l_sc2rf]
+
# Save a flag to indicate whether nextclade is sublineage of sc2rf
nextclade_is_sublineage = False
diff --git a/scripts/plot.py b/scripts/plot.py
index c0e9e42..ba5aa23 100755
--- a/scripts/plot.py
+++ b/scripts/plot.py
@@ -12,6 +12,10 @@
from functions import categorical_palette
import math
import warnings
+from functions import create_logger
+import logging
+
+logging.getLogger("matplotlib.font_manager").disabled = True
warnings.filterwarnings("error")
@@ -33,7 +37,7 @@
# This is the number of characters than can fit width-wise across the legend
LEGEND_FONTSIZE = 6
-LEGEND_CHAR_WIDTH = 100
+LEGEND_CHAR_WIDTH = 90
# The maximum columns in the legend is dictated by the char width, but more
# importantly, in the categorical_palette function, we restrict it to the
# first 5 colors of the tap10 palette, and make different shades within it
@@ -42,6 +46,9 @@
# Show the first N char of the label in the plot
LEGEND_LABEL_MAX_LEN = 15
+# This is the maximum number of key RBD mutations.
+MAX_RBD_LEVEL = 12
+
# Select and rename columns from linelist
LINEAGES_COLS = [
"cluster_id",
@@ -84,10 +91,11 @@
"--lag", help="Reporting lag weeks to draw a grey box", required=False, default=4
)
@click.option(
- "--singletons",
- help="Flag to indicate singleton clusters (N=1) should be reported.",
- is_flag=True,
+ "--min-cluster-size",
+ help="Only plot clusters/lineages with at least this many sequences.",
+ default=1,
)
+@click.option("--log", help="Output log file.", required=False)
def main(
input,
outdir,
@@ -96,16 +104,21 @@ def main(
lag,
min_date,
max_date,
- singletons,
+ min_cluster_size,
+ log,
):
"""Plot recombinant lineages"""
+ # create logger
+ logger = create_logger(logfile=log)
+
# Creat output directory if it doesn't exist
if not os.path.exists(outdir):
os.mkdir(outdir)
# -------------------------------------------------------------------------
# Import dataframes
+ logger.info("Importing linelist: {}".format(input))
df = pd.read_csv(input, sep="\t")
df.fillna(NO_DATA_CHAR, inplace=True)
@@ -129,6 +142,7 @@ def main(
]
# Filter on weeks reporting
+ logger.info("Filtering on data")
if max_date:
max_datetime = datetime.strptime(max_date, "%Y-%m-%d")
max_epiweek = epiweeks.Week.fromdate(max_datetime, system="cdc").startdate()
@@ -165,8 +179,8 @@ def main(
largest_lineage = NO_DATA_CHAR
largest_lineage_size = 0
- # All record cluster sizes to decided if singletons should be dropped
- drop_singleton_ids = []
+ # All record cluster sizes to decided which should be dropped
+ drop_small_clusters_ids = []
for lineage in set(df["recombinant_lineage_curated"]):
match_df = df[df["recombinant_lineage_curated"] == lineage]
@@ -175,13 +189,11 @@ def main(
largest_lineage = match_df["recombinant_lineage_curated"].values[0]
largest_lineage_size = lineage_size
- if lineage_size == 1:
+ if lineage_size < min_cluster_size:
for i in match_df.index:
- drop_singleton_ids.append(i)
+ drop_small_clusters_ids.append(i)
- # now decided if we should drop singletons
- if not singletons:
- df.drop(labels=drop_singleton_ids, axis="rows", inplace=True)
+ df.drop(labels=drop_small_clusters_ids, axis="rows", inplace=True)
df["parents_clade"] = df["parents_clade"].fillna("Unknown")
df["parents_lineage"] = df["parents_lineage"].fillna("Unknown")
@@ -190,6 +202,7 @@ def main(
# Pivot Tables
# -------------------------------------------------------------------------
+ logger.info("Creating pivot tables")
# -------------------------------------------------------------------------
# All
all_df = pd.pivot_table(
@@ -255,6 +268,8 @@ def main(
}
for plot in plot_dict:
+
+ logger.info("Creating plot data: {}".format(plot))
columns = plot_dict[plot]["cols"]
# Several plots need special filtering
@@ -288,13 +303,18 @@ def main(
# Fill in missing levels for RBD
if plot == "rbd_level" and len(plot_df) > 0:
- min_level = min(plot_df.columns)
- max_level = max(plot_df.columns)
+ # min_level = min(plot_df.columns)
+ # max_level = max(plot_df.columns)
+ min_level = 0
+ max_level = MAX_RBD_LEVEL
for level in range(min_level, max_level + 1, 1):
if level not in plot_df.columns:
plot_df[level] = 0
+ # Defragment dataframe for Issue #218
+ plot_df = copy.copy(plot_df)
+
# Add epiweeks column
plot_df.insert(0, "epiweek", plot_df.index)
@@ -333,6 +353,8 @@ def main(
for plot in plot_dict:
+ logger.info("Creating plot figure: {}".format(plot))
+
plot_df = plot_dict[plot]["df"]
x = "epiweek"
@@ -502,11 +524,14 @@ def main(
max_char_len = len(str(col))
legend_ncol = math.floor(LEGEND_CHAR_WIDTH / max_char_len)
+
# we don't want too many columns
if legend_ncol > LEGEND_MAX_COL:
legend_ncol = LEGEND_MAX_COL
elif legend_ncol > num_cat:
legend_ncol = num_cat
+ elif legend_ncol == 0:
+ legend_ncol = 1
legend = ax.legend(
title=legend_title.title(),
@@ -522,7 +547,11 @@ def main(
# Truncate long labels in the legend
# But only if this plots does not involve plotting parents!
# Because we need to see the 2+ parent listed at the end
- if "parents" not in label:
+ if (
+ "parents" not in label
+ and "geography" not in label
+ and "largest" not in label
+ ):
for i in range(0, len(legend.get_texts())):
l_label = legend.get_texts()[i].get_text()
@@ -564,9 +593,14 @@ def main(
FIGSIZE[0] * upscale_factor,
FIGSIZE[1] * upscale_factor,
)
- plt.tight_layout()
- plt.savefig(out_path + ".png")
- plt.savefig(out_path + ".svg")
+
+ # Attempt to see whether we can apply a tight layout
+ try:
+ plt.tight_layout()
+ plt.savefig(out_path + ".png")
+ plt.savefig(out_path + ".svg")
+ except UserWarning:
+ logger.info("Unable to apply tight_layout, plot will not be saved.")
if __name__ == "__main__":
diff --git a/scripts/plot_breakpoints.py b/scripts/plot_breakpoints.py
index 84ec5de..14c359e 100755
--- a/scripts/plot_breakpoints.py
+++ b/scripts/plot_breakpoints.py
@@ -24,7 +24,7 @@
# This is the number of characters than can fit width-wise across the legend
LEGEND_FONTSIZE = 6
-LEGEND_CHAR_WIDTH = 100
+LEGEND_CHAR_WIDTH = 90
# The maximum columns in the legend is dictated by the char width, but more
# importantly, in the categorical_palette function, we restrict it to the
# first 5 colors of the tap10 palette, and make different shades within it
@@ -119,6 +119,7 @@ def main(
if cluster_col:
+ lineages_df[cluster_col] = [str(c) for c in lineages_df[cluster_col]]
# Filter dataframe on cluster IDs
if clusters:
clusters_list = clusters.split(",")
@@ -138,6 +139,9 @@ def main(
if positives:
positives_df = pd.read_csv(positives, sep="\t")
+ positives_df["strain"] = [str(s) for s in positives_df["strain"]]
+ if cluster_col:
+ positives_df[cluster_col] = [str(s) for s in positives_df[cluster_col]]
positives_df.fillna(NO_DATA_CHAR, inplace=True)
# Figsize format back to list
diff --git a/scripts/report.py b/scripts/report.py
index 5b803d9..af676ff 100755
--- a/scripts/report.py
+++ b/scripts/report.py
@@ -37,9 +37,9 @@
"--geo", help="Geography column to summarize", required=False, default="country"
)
@click.option(
- "--singletons",
- help="Whether singletons were included in plots",
- is_flag=True,
+ "--min-cluster-size",
+ help="The minimum size of clusters included in the plots",
+ default=1,
)
@click.option(
"--template",
@@ -53,7 +53,7 @@ def main(
template,
geo,
output,
- singletons,
+ min_cluster_size,
):
"""Create a report of powerpoint slides"""
@@ -70,9 +70,7 @@ def main(
plot_suffix = ".png"
df_suffix = ".tsv"
labels = [
- f.replace(plot_suffix, "")
- for f in os.listdir(plot_dir)
- if f.endswith(plot_suffix)
+ f.replace(df_suffix, "") for f in os.listdir(plot_dir) if f.endswith(df_suffix)
]
plot_dict = {}
@@ -114,7 +112,6 @@ def main(
# General Summary
# Slide setup
- plot_path = plot_dict["lineage"]["plot_path"]
graph_slide_layout = presentation.slide_layouts[8]
slide = presentation.slides.add_slide(graph_slide_layout)
title = slide.shapes.title
@@ -122,7 +119,10 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["lineage"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
# Stats
@@ -159,14 +159,9 @@ def main(
# Construct the summary text
summary = "\n"
- summary += "There are {total_lineages} recombinant lineages".format(
+ summary += "There are {total_lineages} recombinant lineages*.\n".format(
total_lineages=total_lineages
)
- # Whether we need a footnote for singletons
- if singletons:
- summary += ".\n"
- else:
- summary += "*.\n"
for status in RECOMBINANT_STATUS:
if status in status_counts:
@@ -178,16 +173,10 @@ def main(
)
summary += "\n"
- summary += "There are {total_sequences} recombinant sequences".format(
+ summary += "There are {total_sequences} recombinant sequences*.\n".format(
total_sequences=total_sequences
)
- # Whether we need a footnote for singletons
- if singletons:
- summary += ".\n"
- else:
- summary += "*.\n"
-
for status in RECOMBINANT_STATUS:
if status in status_counts:
count = status_counts[status]["sequences"]
@@ -196,9 +185,9 @@ def main(
summary += " - {sequences} sequences are {status}.\n".format(
sequences=count, status=status
)
- if not singletons:
- summary += "\n"
- summary += "*Excluding singleton lineages (N=1)"
+
+ # Add a footnote to indicate cluster size
+ summary += "*Excluding small lineages (N<{})".format(min_cluster_size)
body.text_frame.text = summary
@@ -216,8 +205,6 @@ def main(
if status not in plot_dict:
continue
- plot_path = plot_dict[status]["plot_path"]
-
# Slide setup
graph_slide_layout = presentation.slide_layouts[8]
slide = presentation.slides.add_slide(graph_slide_layout)
@@ -227,7 +214,11 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict[status]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
+
body = slide.placeholders[2]
# Stats
@@ -243,7 +234,7 @@ def main(
num_status_lineages = len(status_lineages)
summary = "\n"
- summary += "There are {num_status_lineages} {status} lineages.\n".format(
+ summary += "There are {num_status_lineages} {status} lineages*.\n".format(
status=status, num_status_lineages=num_status_lineages
)
@@ -253,6 +244,7 @@ def main(
lineage=lineage, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
body.text_frame.text = summary
# Adjust font size of body
@@ -263,7 +255,6 @@ def main(
# ---------------------------------------------------------------------
# Geographic Summary
- plot_path = plot_dict["geography"]["plot_path"]
geo_df = plot_dict["geography"]["df"]
geos = list(geo_df.columns)
geos.remove("epiweek")
@@ -281,11 +272,15 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["geography"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
+
body = slide.placeholders[2]
summary = "\n"
- summary += "Recombinants are observed in {num_geos} {geo}.\n".format(
+ summary += "Recombinants are observed in {num_geos} {geo}*.\n".format(
num_geos=num_geos, geo=geo
)
@@ -295,6 +290,8 @@ def main(
region=region, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
+
body.text_frame.text = summary
# Adjust font size of body
@@ -305,7 +302,6 @@ def main(
# ---------------------------------------------------------------------
# Largest Summary
- plot_path = plot_dict["largest"]["plot_path"]
largest_df = plot_dict["largest"]["df"]
largest_geos = list(largest_df.columns)
@@ -337,11 +333,14 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["largest"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
summary = "\n"
- summary += "The largest lineage is {lineage} (N={size}).\n".format(
+ summary += "The largest lineage is {lineage} (N={size})*.\n".format(
lineage=largest_lineage,
size=largest_lineage_size,
)
@@ -356,6 +355,8 @@ def main(
region=region, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
+
body.text_frame.text = summary
# Adjust font size of body
@@ -366,12 +367,15 @@ def main(
# ---------------------------------------------------------------------
# RBD Levels
- plot_path = plot_dict["rbd_level"]["plot_path"]
rbd_df = plot_dict["rbd_level"]["df"]
rbd_levels = list(rbd_df.columns)
rbd_levels.remove("epiweek")
# Order columns
- rbd_counts = {level: int(sum(rbd_df[level])) for level in rbd_levels}
+ rbd_counts = {
+ level: int(sum(rbd_df[level]))
+ for level in rbd_levels
+ if int(sum(rbd_df[level])) > 0
+ }
rbd_levels = dict(sorted(rbd_counts.items()))
num_rbd_levels = len(rbd_levels)
@@ -384,11 +388,14 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["rbd_level"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
summary = "\n"
- summary += "{num_rbd_levels} RBD levels are observed.\n".format(
+ summary += "{num_rbd_levels} RBD levels are observed*.\n".format(
num_rbd_levels=num_rbd_levels,
)
@@ -398,6 +405,7 @@ def main(
level=level, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
body.text_frame.text = summary
# Adjust font size of body
@@ -408,7 +416,6 @@ def main(
# ---------------------------------------------------------------------
# Parents Summary (Clade)
- plot_path = plot_dict["parents_clade"]["plot_path"]
parents_df = plot_dict["parents_clade"]["df"]
parents = list(parents_df.columns)
@@ -427,11 +434,14 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["parents_clade"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
summary = "\n"
- summary += "There are {num_parents} clade combinations.\n".format(
+ summary += "There are {num_parents} clade combinations*.\n".format(
num_parents=num_parents
)
@@ -441,6 +451,7 @@ def main(
parent=parent, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
body.text_frame.text = summary
# Adjust font size of body
@@ -451,7 +462,6 @@ def main(
# ---------------------------------------------------------------------
# Parents Summary (Lineage)
- plot_path = plot_dict["parents_lineage"]["plot_path"]
parents_df = plot_dict["parents_lineage"]["df"]
parents = list(parents_df.columns)
@@ -470,11 +480,14 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["parents_lineage"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
summary = "\n"
- summary += "There are {num_parents} lineage combinations.\n".format(
+ summary += "There are {num_parents} lineage combinations*.\n".format(
num_parents=num_parents
)
@@ -484,6 +497,7 @@ def main(
parent=parent, seq_count=seq_count
)
+ summary += "\n*Excluding small lineages (N<{})".format(min_cluster_size)
body.text_frame.text = summary
# Adjust font size of body
@@ -494,8 +508,6 @@ def main(
# ---------------------------------------------------------------------
# Breakpoints Clade Summary
- plot_path = plot_dict["breakpoints_clade"]["plot_path"]
-
graph_slide_layout = presentation.slide_layouts[8]
slide = presentation.slides.add_slide(graph_slide_layout)
title = slide.shapes.title
@@ -504,14 +516,15 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["breakpoints_clade"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
# ---------------------------------------------------------------------
# Breakpoints Lineage Summary
- plot_path = plot_dict["breakpoints_lineage"]["plot_path"]
-
graph_slide_layout = presentation.slide_layouts[8]
slide = presentation.slides.add_slide(graph_slide_layout)
title = slide.shapes.title
@@ -520,7 +533,10 @@ def main(
title.text_frame.paragraphs[0].font.bold = True
chart_placeholder = slide.placeholders[1]
- chart_placeholder.insert_picture(plot_path)
+ # Plotting may have failed to create an individual figure
+ plot_path = plot_dict["breakpoints_lineage"]["plot_path"]
+ if os.path.exists(plot_path):
+ chart_placeholder.insert_picture(plot_path)
body = slide.placeholders[2]
# ---------------------------------------------------------------------
diff --git a/scripts/slurm.sh b/scripts/slurm.sh
index 0e39048..db540ff 100755
--- a/scripts/slurm.sh
+++ b/scripts/slurm.sh
@@ -89,12 +89,15 @@ fi
mkdir -p $log_dir
+# Just in case the conda_env is an absolute path
+job_name=$(basename $conda_env)
+
cmd="
sbatch
--parsable
-c ${cpus}
--mem=${mem}
- -J ${conda_env}
+ -J ${job_name}
-o ${log_dir}/%x_${today}_%j.log
--wrap=\"source activate $conda_env && snakemake --profile $profile $target\"
"
diff --git a/scripts/validate.py b/scripts/validate.py
index b84c7f2..fe05dac 100644
--- a/scripts/validate.py
+++ b/scripts/validate.py
@@ -4,7 +4,7 @@
import os
NO_DATA_CHAR = "NA"
-VALIDATE_COLS = ["lineage", "parents_clade", "breakpoints"]
+VALIDATE_COLS = ["status", "lineage", "parents_clade", "breakpoints"]
@click.command()
diff --git a/setup.cfg b/setup.cfg
index 0e185f2..0db4fa8 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -6,6 +6,7 @@ per-file-ignores =
scripts/pull_table.py:B950
scripts/report.py:B950,W605
scripts/pango_lineage_tree.py:B950
+ docs/sphinx/source/conf.py:F401
ignore =
# Use bugbear line length detection instead of default
E501,
diff --git a/workflow/Snakefile b/workflow/Snakefile
index 8627b0f..b8d2e83 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -13,6 +13,7 @@ import datetime
import subprocess
import pandas as pd
import json
+import re
# Enforce minimum version
from snakemake.utils import min_version
@@ -107,6 +108,17 @@ def _inputs(build):
# Default Target
# ------------------------------------------------------------------------------
+report_targets = []
+
+for build in BUILDS:
+
+ # Identify the types of reports and plots
+ for rule_params in config["builds"][build]:
+ if rule_params.startswith("plot"):
+ report_dir = rule_params.replace("plot", "report")
+ report_path = os.path.join("results", build, report_dir, "report.pptx")
+ report_targets.append(report_path)
+
rule all:
"""
Default workflow targets.
@@ -125,12 +137,7 @@ rule all:
build_name=BUILDS,
),
# Stage 3: Reports
- expand("results/{build_name}/report/report.xlsx",
- build_name=BUILDS,
- ),
- expand("results/{build_name}/report_historical/report.pptx",
- build_name=BUILDS,
- ),
+ report_targets,
# Stage 4: Validation
expand("results/{build_name}/validate/validation.tsv",
build_name=BUILDS,
@@ -263,78 +270,58 @@ rule issues_download:
"""
# -----------------------------------------------------------------------------
-rule_name = "nextclade_dataset"
-rule nextclade_dataset:
- """Download Nextclade dataset."""
+rule_name = "lineage_tree"
+rule lineage_tree:
+ """Construct a nomenclature tree of lineages."""
- message: """Downloading Nextclade dataset.\n
+ message: """Constructing a nomenclature tree of lineages.\n
log: {log}
- dataset: {params.dataset_dir}
+ tree: {output.tree}
"""
wildcard_constraints:
# The tag will always begin with the year (ex. 2022)
tag = "([0-9]){4}.*",
output:
- #dataset_dir = directory("data/{dataset}_{tag}"),
- genemap = "data/{dataset}_{tag}/genemap.gff",
- primers = "data/{dataset}_{tag}/primers.csv",
- qc = "data/{dataset}_{tag}/qc.json",
- reference = "data/{dataset}_{tag}/reference.fasta",
- sequences = "data/{dataset}_{tag}/sequences.fasta",
- tag = "data/{dataset}_{tag}/tag.json",
- tree = "data/{dataset}_{tag}/tree.json",
- properties = "data/{dataset}_{tag}/virus_properties.json",
- params:
- dataset_dir = "data/{dataset}_{tag}",
+ tree = "resources/tree.nwk",
threads: 1
resources:
cpus = 1,
benchmark:
- "benchmarks/{rule}/{{dataset}}_{{tag}}_{today}.tsv".format(today=today, rule=rule_name),
+ "benchmarks/{rule}/{today}.tsv".format(today=today, rule=rule_name),
log:
- "logs/{rule}/{{dataset}}_{{tag}}_{today}.log".format(today=today, rule=rule_name),
+ "logs/{rule}/{today}.log".format(today=today, rule=rule_name),
shell:
"""
- # Download dataset
- nextclade dataset get --name {wildcards.dataset} --tag {wildcards.tag} --output-dir {params.dataset_dir} > {log} 2>&1;
+ python3 scripts/lineage_tree.py --output {output.tree} > {log};
"""
# -----------------------------------------------------------------------------
-rule_name = "lineage_tree"
-rule lineage_tree:
- """Construct a tree of Nextclade lineages."""
+rule_name = "nextclade_dataset"
+rule nextclade_dataset:
+ """Download Nextclade dataset."""
- message: """Constructing a tree of Nextclade lineages.\n
+ message: """Downloading Nextclade dataset.\n
log: {log}
- json: {input.tree}
- tree: {output.tree}
+ dataset: {output.dataset_dir}
"""
wildcard_constraints:
# The tag will always begin with the year (ex. 2022)
tag = "([0-9]){4}.*",
- input:
- #json = lambda wildcards: "data/{dataset}_{tag}/tree.json".format(
- # dataset = config["builds"][wildcards.build]["nextclade_dataset"]["dataset"],
- # tag = config["builds"][wildcards.build]["nextclade_dataset"]["tag"],
- # ),
- tree = "data/{dataset}_{tag}/tree.json",
output:
- # tree = "results/{build}/lineage_tree/tree.nwk",
- tree = "data/{dataset}_{tag}/tree.nwk",
+ dataset_dir = directory("data/{dataset}_{tag}"),
threads: 1
resources:
cpus = 1,
benchmark:
- #"benchmarks/{rule}/{{build}}_{today}.tsv".format(today=today, rule=rule_name),
"benchmarks/{rule}/{{dataset}}_{{tag}}_{today}.tsv".format(today=today, rule=rule_name),
log:
- #"logs/{rule}/{{build}}_{today}.log".format(today=today, rule=rule_name),
"logs/{rule}/{{dataset}}_{{tag}}_{today}.log".format(today=today, rule=rule_name),
shell:
"""
- python3 scripts/lineage_tree.py --tree-json {input.tree} --output {output.tree} --log {log};
+ # Download dataset
+ nextclade dataset get --name {wildcards.dataset} --tag {wildcards.tag} --output-dir {output.dataset_dir} > {log} 2>&1;
"""
# ------------------------------------------------------------------------------
@@ -375,9 +362,7 @@ rule nextclade:
wildcard_constraints:
nextclade_prefix = "nextclade|nextclade_no-recomb|nextclade_immune-escape"
input:
- # This is not ideal to just use one file (instead of the full dataset dir)
- # But I'm avoiding using a full directory as I/O to support lineage_tree rule
- properties = lambda wildcards: "data/{dataset}_{tag}/virus_properties.json".format(
+ dataset = lambda wildcards: "data/{dataset}_{tag}".format(
dataset=_inputs_nextclade(wildcards.build, wildcards.nextclade_prefix)["dataset"],
tag=_inputs_nextclade(wildcards.build, wildcards.nextclade_prefix)["tag"],
),
@@ -388,10 +373,6 @@ rule nextclade:
qc = "results/{build}/{nextclade_prefix}/qc.tsv",
metadata = "results/{build}/{nextclade_prefix}/metadata.tsv",
params:
- dataset = lambda wildcards: "data/{dataset}_{tag}".format(
- dataset=_inputs_nextclade(wildcards.build, wildcards.nextclade_prefix)["dataset"],
- tag=_inputs_nextclade(wildcards.build, wildcards.nextclade_prefix)["tag"],
- ),
outdir = "results/{build}/{nextclade_prefix}",
basename = "nextclade",
selection = "fasta,tsv"
@@ -404,7 +385,7 @@ rule nextclade:
# Align sequences
nextclade run \
--jobs {resources.cpus} \
- --input-dataset {params.dataset} \
+ --input-dataset {input.dataset} \
--output-all {params.outdir} \
--output-selection {params.selection} \
--output-tsv {output.qc} \
@@ -541,6 +522,7 @@ def _params_sc2rf(build):
# Add the arguments for each mode
params["sc2rf_args"] = {}
+
for mode_args in config["builds"][build]["sc2rf"]["mode"]:
mode = list(mode_args.keys())[0]
@@ -663,6 +645,10 @@ def _params_sc2rf_recombinants(build):
if lapis: params["lapis"] = "--lapis"
else: params["lapis"] = ""
+ gisaid_access_key = config["builds"][build]["sc2rf_recombinants"]["gisaid_access_key"]
+ if gisaid_access_key: params["gisaid_access_key"] = "--gisaid-access-key {}".format(gisaid_access_key)
+ else: params["gisaid_access_key"] = ""
+
# The metadata param will not be used if we are excluding negatives
metadata = _inputs(build)["metadata"]
exclude_negatives = config["builds"][build]["sc2rf"]["exclude_negatives"]
@@ -690,10 +676,7 @@ rule sc2rf_recombinants:
issues = rules.issues_download.output.issues,
nextclade = "results/{build}/nextclade/qc.tsv",
nextclade_no_recomb = "results/{build}/nextclade_no-recomb/qc.tsv",
- lineage_tree = lambda wildcards: "data/{dataset}_{tag}/tree.nwk".format(
- dataset=_inputs_nextclade(wildcards.build, "nextclade")["dataset"],
- tag=_inputs_nextclade(wildcards.build, "nextclade")["tag"],
- ),
+ lineage_tree = rules.lineage_tree.output.tree,
metadata = lambda wildcards: _inputs(wildcards.build)["metadata"],
output:
stats = "results/{build}/sc2rf/stats.tsv",
@@ -713,6 +696,7 @@ rule sc2rf_recombinants:
dup_method = lambda wildcards: _params_sc2rf_recombinants(wildcards.build)["dup_method"],
auto_pass = lambda wildcards: _params_sc2rf_recombinants(wildcards.build)["auto_pass"],
lapis = lambda wildcards: _params_sc2rf_recombinants(wildcards.build)["lapis"],
+ gisaid_access_key = lambda wildcards: _params_sc2rf_recombinants(wildcards.build)["gisaid_access_key"],
metadata = lambda wildcards: _params_sc2rf_recombinants(wildcards.build)["metadata"],
# Join inputs together with commas
ansi = lambda wildcards: ",".join(_inputs_sc2rf_recombinants(wildcards.build)["ansi"]),
@@ -746,6 +730,7 @@ rule sc2rf_recombinants:
{params.max_breakpoints} \
{params.dup_method} \
{params.lapis} \
+ {params.gisaid_access_key} \
--log {log} \
>> {log} 2>&1;
@@ -851,10 +836,7 @@ rule linelist:
input:
summary = rules.summary.output.summary,
issues = rules.issues_download.output.issues,
- lineage_tree = lambda wildcards: "data/{dataset}_{tag}/tree.nwk".format(
- dataset=_inputs_nextclade(wildcards.build, "nextclade")["dataset"],
- tag=_inputs_nextclade(wildcards.build, "nextclade")["tag"],
- ),
+ lineage_tree = rules.lineage_tree.output.tree,
output:
linelist = "results/{build}/linelists/linelist.tsv",
positives = "results/{build}/linelists/positives.tsv",
@@ -901,28 +883,49 @@ rule linelist:
rule_name = "plot"
# Parameters Function
-def _params_plot(build):
+def _params_plot(build, plot_type):
"""Parse parameters from wildcards for rule plot."""
params = {}
- weeks = config["builds"][build]["plot"]["weeks"]
+ custom_cluster_size = re.search(".*N([0-9]*$)", plot_type)
+ is_historical = "historical" in plot_type
+ is_report = "report" in plot_type
+
+ if not is_historical:
+ plot_rule = "plot"
+ params["autoscale"] = ""
+ else:
+ plot_rule = "plot_historical"
+ params["autoscale"] = "--autoscale"
+
+ if not is_report:
+ params["plot_dir"] = os.path.join("results", build, plot_type)
+ else:
+ params["plot_dir"] = os.path.join("results", build, plot_type.replace("report", "plots"))
+
+ weeks = config["builds"][build][plot_rule]["weeks"]
if weeks: params["weeks"] = "--weeks {}".format(weeks)
else: params["weeks"] = ""
- min_date = config["builds"][build]["plot"]["min_date"]
+ min_date = config["builds"][build][plot_rule]["min_date"]
if min_date: params["min_date"] = "--min-date {}".format(min_date)
else: params["min_date"] = ""
- max_date = config["builds"][build]["plot"]["max_date"]
+ max_date = config["builds"][build][plot_rule]["max_date"]
if max_date: params["max_date"] = "--max-date {}".format(max_date)
else: params["max_date"] = ""
- singletons = config["builds"][build]["plot"]["singletons"]
- if singletons: params["singletons"] = "--singletons"
- else: params["singletons"] = ""
+ if not custom_cluster_size:
+ min_cluster_size = config["builds"][build][plot_rule]["min_cluster_size"]
+ else:
+ min_cluster_size = re.findall("N([0-9]*)$", plot_type)[0]
+
+ if min_cluster_size: params["min_cluster_size"] = "--min-cluster-size {}".format(min_cluster_size)
+ else: params["min_cluster_size"] = ""
+
- lag = config["builds"][build]["plot"]["lag"]
+ lag = config["builds"][build][plot_rule]["lag"]
if lag: params["lag"] = "--lag {}".format(lag)
else: params["lag"] = ""
@@ -938,37 +941,39 @@ rule plot:
plots: {output.plots}
"""
+ wildcard_constraints:
+ plot_type = "plots|plots_historical|plots_N[0-9]*|plots_historical_N[0-9]*",
input:
- positives = rules.linelist.output.positives,
- lineages = rules.linelist.output.lineages,
+ positives = rules.linelist.output.positives,
+ lineages = rules.linelist.output.lineages,
output:
- plots = directory("results/{build}/plots"),
+ plots = directory("results/{build}/{plot_type}"),
params:
- outdir = "results/{build}/plots",
- geo = lambda wildcards: _params_linelist(wildcards.build)["geo"],
- weeks = lambda wildcards: _params_plot(wildcards.build)["weeks"],
- min_date = lambda wildcards: _params_plot(wildcards.build)["min_date"],
- max_date = lambda wildcards: _params_plot(wildcards.build)["max_date"],
- lag = lambda wildcards: _params_plot(wildcards.build)["lag"],
- singletons = lambda wildcards: _params_plot(wildcards.build)["singletons"],
+ geo = lambda wildcards: _params_linelist(wildcards.build)["geo"],
+ weeks = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["weeks"],
+ min_date = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["min_date"],
+ max_date = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["max_date"],
+ lag = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["lag"],
+ min_cluster_size = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["min_cluster_size"],
+ autoscale = lambda wildcards: _params_plot(wildcards.build, wildcards.plot_type)["autoscale"],
threads: 1
resources:
cpus = 1,
benchmark:
- "benchmarks/{rule}/{{build}}_{today}.tsv".format(today=today, rule=rule_name),
+ "benchmarks/{{plot_type}}/{{build}}_{today}.tsv".format(today=today),
log:
- "logs/{rule}/{{build}}_{today}.log".format(today=today, rule=rule_name),
+ "logs/{{plot_type}}/{{build}}_{today}.log".format(today=today),
shell:
"""
python3 scripts/plot.py \
--input {input.positives} \
- --outdir {params.outdir} \
+ --outdir {output.plots} \
{params.lag} \
{params.geo} \
{params.weeks} \
{params.min_date} \
{params.max_date} \
- {params.singletons} \
+ {params.min_cluster_size} \
> {log} 2>&1;
# Extract the cluster IDs to be plotted
@@ -979,11 +984,12 @@ rule plot:
--lineages {input.lineages} \
--lineage-col recombinant_lineage_curated \
--positives {input.positives} \
- --outdir {params.outdir} \
+ --outdir {output.plots} \
--parent-col parents_clade \
--parent-type clade \
--cluster-col cluster_id \
--clusters "${{cluster_ids}}" \
+ {params.autoscale} \
>> {log} 2>&1;
# Plot breakpoints by lineage
@@ -991,76 +997,16 @@ rule plot:
--lineages {input.lineages} \
--lineage-col recombinant_lineage_curated \
--positives {input.positives} \
- --outdir {params.outdir} \
+ --outdir {output.plots} \
--parent-col parents_lineage \
--parent-type lineage \
--cluster-col cluster_id \
--clusters "${{cluster_ids}}" \
+ {params.autoscale} \
>> {log} 2>&1;
"""
-# ------------------------------------------------------------------------------
-rule_name = "plot_historical"
-rule plot_historical:
- """Plot results as a historical timeline."""
-
- message: """Plot results as a historical timeline.\n
- build: {wildcards.build}
- log: {log}
- plots: {output.plots}
- """
-
- input:
- positives = rules.linelist.output.positives,
- lineages = rules.linelist.output.lineages,
- output:
- plots = directory("results/{build}/plots_historical"),
- params:
- outdir = "results/{build}/plots_historical",
- geo = lambda wildcards: _params_linelist(wildcards.build)["geo"],
- lag = lambda wildcards: _params_plot(wildcards.build)["lag"],
- singletons = lambda wildcards: _params_plot(wildcards.build)["singletons"],
- threads: 1
- resources:
- cpus = 1,
- benchmark:
- "benchmarks/{rule}/{{build}}_{today}.tsv".format(today=today, rule=rule_name),
- log:
- "logs/{rule}/{{build}}_{today}.log".format(today=today, rule=rule_name),
- shell:
- """
- python3 scripts/plot.py \
- --input {input.positives} \
- --outdir {params.outdir} \
- {params.lag} \
- {params.geo} \
- {params.singletons} \
- > {log} 2>&1;
-
- # Plot breakpoints by clade
- python3 scripts/plot_breakpoints.py \
- --lineages {input.lineages} \
- --lineage-col recombinant_lineage_curated \
- --positives {input.positives} \
- --outdir {params.outdir} \
- --parent-col parents_clade \
- --parent-type clade \
- --cluster-col cluster_id \
- --autoscale \
- >> {log} 2>&1;
-
- # Plot breakpoints by lineage
- python3 scripts/plot_breakpoints.py \
- --lineages {input.lineages} \
- --lineage-col recombinant_lineage_curated \
- --positives {input.positives} \
- --outdir {params.outdir} \
- --parent-col parents_lineage \
- --parent-type lineage \
- --cluster-col cluster_id \
- --autoscale \
- >> {log} 2>&1;
- """
+ # --autoscale for x clusters
# ------------------------------------------------------------------------------
# Report
@@ -1093,8 +1039,10 @@ rule report:
slides: {output.pptx}
"""
+ wildcard_constraints:
+ report_type = "report|report_historical|report_N[0-9]*|report_historical_N[0-9]*",
input:
- plots = rules.plot.output.plots,
+ plots = lambda wildcards: _params_plot(wildcards.build, wildcards.report_type)["plot_dir"],
linelist = rules.linelist.output.linelist,
tables = [
rules.linelist.output.lineages,
@@ -1107,59 +1055,27 @@ rule report:
rules.issues_download.output.issues,
],
output:
- pptx = "results/{build}/report/report.pptx",
- xlsx = "results/{build}/report/report.xlsx",
+ pptx = "results/{build}/{report_type}/report.pptx",
+ xlsx = "results/{build}/{report_type}/report.xlsx",
params:
- geo = lambda wildcards: _params_report(wildcards.build)["geo"],
- template = lambda wildcards: _params_report(wildcards.build)["template"],
- singletons = lambda wildcards: _params_plot(wildcards.build)["singletons"],
+ geo = lambda wildcards: _params_report(wildcards.build)["geo"],
+ template = lambda wildcards: _params_report(wildcards.build)["template"],
+ min_cluster_size = lambda wildcards: _params_plot(wildcards.build, wildcards.report_type)["min_cluster_size"],
threads: 1
resources:
cpus = 1,
benchmark:
- "benchmarks/{rule}/{{build}}_{today}.tsv".format(today=today, rule=rule_name),
+ "benchmarks/{{report_type}}/{{build}}_{today}.tsv".format(today=today),
log:
- "logs/{rule}/{{build}}_{today}.log".format(today=today, rule=rule_name),
+ "logs/{{report_type}}/{{build}}_{today}.log".format(today=today),
shell:
"""
# Create the excel report
csvtk csv2xlsx -t -o {output.xlsx} {input.tables} > {log} 2>&1;
# Create the powerpoint slides
- python3 scripts/report.py --linelist {input.linelist} --plot-dir {input.plots} --output {output.pptx} {params.geo} {params.template} {params.singletons} >> {log} 2>&1;
- """
-
-rule report_historical:
- """Summarize results into a historical report."""
-
- message: """Summarize results into a historical report.\n
- build: {wildcards.build}
- log: {log}
- slides: {output.pptx}
- """
-
- input:
- plots = rules.plot_historical.output.plots,
- linelist = rules.linelist.output.linelist,
- output:
- pptx = "results/{build}/report_historical/report.pptx",
- params:
- geo = lambda wildcards: _params_report(wildcards.build)["geo"],
- template = lambda wildcards: _params_report(wildcards.build)["template"],
- singletons = lambda wildcards: _params_plot(wildcards.build)["singletons"],
-
- threads: 1
- resources:
- cpus = 1,
- benchmark:
- "benchmarks/{rule}/{{build}}_{today}.tsv".format(today=today, rule=rule_name),
- log:
- "logs/{rule}/{{build}}_{today}.log".format(today=today, rule=rule_name),
- shell:
- """
- # Create the powerpoint slides
- python3 scripts/report.py --linelist {input.linelist} --plot-dir {input.plots} --output {output.pptx} {params.geo} {params.template} {params.singletons} > {log} 2>&1;
+ python3 scripts/report.py --linelist {input.linelist} --plot-dir {input.plots} --output {output.pptx} {params.geo} {params.template} {params.min_cluster_size} >> {log} 2>&1;
"""
# ------------------------------------------------------------------------------
diff --git a/workflow/envs/environment.yaml b/workflow/envs/environment.yaml
index f25ce94..162ce04 100644
--- a/workflow/envs/environment.yaml
+++ b/workflow/envs/environment.yaml
@@ -17,22 +17,29 @@ dependencies:
- bioconda::csvtk=0.24.0
- bioconda::seqkit=2.2.0
# Alignmnent and Clade QC
- - bioconda::nextclade=2.8.0
+ - bioconda::nextclade=2.9.1
# Phylogeny
- conda-forge::biopython=1.79
# Visualization
- conda-forge::seaborn=0.12.0
- bioconda::snipit=1.0.7
- plotly::plotly=5.10.0
+ - conda-forge::python-kaleido=0.2.1
# Report Generation
- conda-forge:python-pptx=0.6.21
- bioconda::epiweeks=2.1.4
- conda-forge::zip=3.0
# For dev users
- #- conda-forge::pre_commit=2.17.0
- #- anaconda::git=2.35.0
- #- conda-forge::yarn
+ - conda-forge::pre_commit=2.17.0
+ - conda-forge::git=2.35.0
+ # Documentation=
+ - conda-forge::yarn=1.22.19
+ - conda-forge::sphinx=5.3.0
+ - conda-forge::m2r=0.3.1
+ - conda-forge::myst-parser=0.18.1
+ - conda-forge::sphinx_rtd_theme=1.1.1
- pip:
# sc2rf dependencies
- termcolor==1.1.0
- tqdm==4.63.0
+ - pango_aliasor==0.2.2
+
+
+