VLQ-nf

Estimates SARS-CoV-2 lineage abundances from wastewater samples. Here, we provide a Nextflow pipeline implementation of the original idea by Baaijens, Zulli, Ott et al. Please check their manuscript and code and acknowledge their work and cite their paper when using anything from this repository!

Summary

The goal is to detect SARS-CoV-2 lineages from wastewater samples and estimate the abundance of each lineage within a sample. The implemented method repurposes the Kallisto tool like first presented by Baaijens, Zulli, Ott et al. In a first step, a reference data set containing multiple sequences for multiple SARS-CoV-2 lineages is reconstructed from GISAID data. In the second step, a Kallisto index is built and FASTQ samples are pseudo-aligned to estimate the abundance of each reference lineage in the samples.

Program Composition

1. Reference building

Process	Description
process_gisaid.nf	Cleanse input data set and create consistent scheme: human samples containing information on sample origin, date and lineage annotation.
filter_by_metadata.nf	Filter records by geographic origin, sampling date, number of ambiguous bases. Sample X genomic sequences per lineage.
filter_sequences.nf	Extract sequence data for selected samples from input FASTA.
variant_call.nf	Call variants.
merge_vcf.nf	Merge VCF data per lineage.
filter_by_aaf.nf	Filter samples for each lineage, so that those variants that were observed in more than Y% of the variant called sequences are represented at least once.
filter_sequences_by_aaf.nf	Extact sequence data for samples selected in AAF-based filtering.

2. Abundance estimatation

Process	Description
build_index.nf	Build kallisto index structure from the final reference data set
kallisto_prediction_(single,paired).nf	Quantify lineage abundances per input sample

Requirements

This tool was developed and tested on Nextflow (v.21.10.6) and Conda (v.4.12.0).

Usage

First, create a conda environment ensuring all required packages and versions. Activate and run workflow in the conda env:

conda create -n sc2-sewage -c bioconda -c anaconda -c conda-forge bcftools=1.3.1 biopython=1.78 htslib=1.3.1 kallisto=0.46.0 minimap2=2.17 pandas=1.1.3 pyvcf seqtk=1.3 gzip glob2=0.7
conda activate sc2-sewage

Execute program like this to reconstruct a reference data set and using kallisto to analyse your query samples:

• be sure to always use full file paths
• check which parameters (e.g. output locations) might have default values set in nextflow.config

nextflow run main.nf --gisaid PATH_TO_GISAID_DATA/ --query PATH_TO_SEQ_DATA/ --country Germany,England --startdate 2021-02-01 --enddate 2021-04-30

If you want to reuse an already processed reference data set, use like this:

• be aware, that existing reference csv and fasta files are looked up in the directory defined by --databases

nextflow run main.nf --reference true --query PATH_TO_SEQ_DATA/

Default settings for all parameters can be found in nextflow.config

Example

The example/ directory contains a small example from https://github.com/baymlab/wastewater_analysis to test the pipeline. The examplary reference metadata and sequence files are stored in example/resource/, the wastewater sequencing data in example/samples/. In order to work with this implementation, we adjusted the metdata scheme and fasta headers according to the required data structure.

nextflow run main.nf --gisaid PATH_TO/example/resource --query PATH_TO/example/samples --k 10 --single_end false --output PATH_TO/example/results --runinfo PATH_TO/example/runinfo --databases PATH_TO/example/databases

The predictions can be found in example/results/input/kallisto_out/predictions.tsv and should show that 100% of this sample is SARS-CoV-2.

Data

• GISAID: download data set with
  • sequence data (fasta, fasta.gz) and
  • metadata (tab-delimited csv) required columns: ['Virus name', 'Accession ID', 'Collection date', 'Location', 'Sequence length', 'Host', 'Pango lineage', 'N-Content'])
• Wastewater samples (fastq, fastq.gz)

Program Handling

nextflow run main.nf --help

Parameter	Type	Description	Default
Mandatory:
- -gisaid	String	Full system path to folder storing metadata and sequence files from gisaid for reference building. Currently, metadata has to be tab-delimited csv or tsv file and sequence info has to be fastaor fasta.gz. Required columns: ['Virus name', 'Accession ID', 'Collection date', 'Location', 'Sequence length', 'Host', 'Pango lineage', 'N-Content']	None
or - -reference	String	If true, the prebuilt reference in --databases is used to build an index and analyse the query	false
- -query	String	Path to folder storing query fastq or fastq.gz files	None
Reference building:
- -continent	List of Strings	List of comma-separated continents to consider when selecting reference samples based on geography. Inform yourself how continents are captured by your input data resource. Whitespaces in country names need to be replaced by underscore. Example: "North_America"	" "
- -country	List of Strings	List of comma-separated countries to consider when selecting reference samples based on geography. Inform yourself how country names are captured by GISAID. Whitespaces in country names need to be replaced by underscore. Example: Bosnia_Herzegovina	None
- -startdate	String	Earliest sampling date YYYY-MM-DD to consider when selecting reference samples based on the timepoint of sample drawing.	" "
- -enddate	String	Latest sampling date YYYY-MM-DD to consider when selecting reference samples based on the timepoint of sample drawing.	" "
- -min_len	Int	Don't select sequences with less than the specified minimal number of non-ambiguous nucleotides.	29500
- -k	Int	Specify how many samples to randomly select per lineage when filtering based on metadata.	1000
- -seed	Int	Random seed for sampling steps during sequence selection	0
- -min_aaf	Float	Minimal alternative allele frequency (AAF) to consider for representative lineage variation.	0.5
- -max_per_lineage	Int	Maximum number of lineages to select to represent a lineages genomic variation.	0
Kallisto
- -single_end	boolean	If true, run kallisto parametrized for single-end reads, else for paired-end reads	true
- -fragment_length	Int	Estimated average fragment length.	200
- -fragment_length_sd	Int	Estimated standard deviation of fragment length.	20
- -kallisto_threads	Int	Number of threads to use.	20
- -bootstrap	int	Determine whether kallisto should be run in bootstrap mode and how many bootstraps should be used.	0
Output
- -min_ab	Float	Summarize output for all lineages whose estimated abundance is above this minimum threshold.	0
Nextflow options		Paths of listed folders need to be relative to location of main.nf
-output	String	path to output folder	sc2-sewage-results/
-runinfo	String	path to folder storing log files	sc2-sewage-runinfo/
-databases	String	path to folder storing intermediate files and reference data	sc2-sewage-databases/

For more information on kallisto, e.g. regarding single and paired-end fastq queries, see https://pachterlab.github.io/kallisto/manual

Output

Results

--output:

• NAME_OF_QUERY_SAMPLE/kallisto_out/ stores the kallisto output fiels and the final output table containing the lineage abundances (predictions.tsv)

--databases:

• build_reference/ stores variant call files as well as the final reference set (fasta and csv)

--runinfo:

• Process-specific log files
• Nextflow tracking files like the pipeline DAG, execution report and timeline.

References

• Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification
• Original GitHub repository: SARS-CoV-2 variant abundance estimation in wastewater