Fix duplicated fasta paths

README.md

@@ -2,6 +2,7 @@
 
 [![](https://img.shields.io/static/v1?label=CLI&message=Snaketool&color=blueviolet)](https://github.com/beardymcjohnface/Snaketool)
 [![license](https://img.shields.io/github/license/metagenlab/mess.svg)](https://github.com/metagenlab/MeSS/blob/main/LICENSE)
+[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/mess/README.html)
 [![version](https://img.shields.io/conda/vn/bioconda/mess?color=blue)](http://bioconda.github.io/recipes/mess/README.html)
 [![downloads](https://img.shields.io/conda/dn/bioconda/mess.svg)](https://anaconda.org/bioconda/mess)
 
@@ -14,7 +15,7 @@
 
 The Metagenomic Sequence Simulator (MeSS) is a [Snakemake](https://github.com/snakemake/snakemake) pipeline, implemented using [Snaketool](https://github.com/beardymcjohnface/Snaketool), for simulating illumina, Oxford Nanopore (ONT) and Pacific Bioscience (PacBio) shotgun metagenomic samples.
 
-## :memo: Overview
+## :mag: Overview
 
 MeSS takes as input NCBI taxa or local genome assemblies to generate either long (PacBio or ONT) or short (illumina) reads. In addition to reads, MeSS optionally generates bam alignment files and taxonomic + sequence abundances in [CAMI format](https://github.com/bioboxes/rfc/blob/master/data-format/profiling.mkd).
 
@@ -25,7 +26,7 @@ input["samples.tsv
 or 
 samples/*.tsv"] --> taxons
 
-subgraph genome_download["genome download"]
+subgraph genome_download["`**genome download**`"]
 dlchoice{download ?}
 taxons["taxons or
 accesions"] --> dlchoice
@@ -35,7 +36,7 @@ assembly_finder --> fasta
 end
 
 input --> distchoice
-subgraph community_design["community design"]
+subgraph community_design["`**community design**`"]
 distchoice{draw distribution ?}
 distchoice -->|yes| dist["distribution 
 (lognormal, even)"]
@@ -58,9 +59,7 @@ simulator --> bam
 simulator --> fastq
 simulator --> CAMI-profile
 
-%% colors
-style genome_download color:black
-style community_design color:black
+%% subgraph color fills
 classDef red fill:#faeaea,color:#fff,stroke:#333;
 classDef blue fill:#eaecfa,color:#fff,stroke:#333;
 class genome_download blue
@@ -71,47 +70,85 @@ class community_design red
 More details can be found in the [documentation](https://metagenlab.github.io/MeSS/)
 
 ## :zap: Quick start 
-### Installation
-
-#### Mamba
-
-[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/mess/README.html)
-
+### :gear: Installation
+Mamba
 ```sh
 mamba create -n mess mess
 ```
 
-#### Docker
-
+Docker
 ```sh
 docker pull ghcr.io/metagenlab/mess:latest
 ```
 
-#### From source 
-
+From source 
 ```sh
 git clone https://github.com/metagenlab/MeSS.git
 pip install -e MeSS
 ```
 
-### Usage
+### :page_facing_up: Usage
+#### :arrow_right: Input
+Let's simulate two metagenomic samples with the following taxa and read counts in `samples.tsv`:
+| sample   | taxon | reads  |
+| ---      | ---   | ---    |
+| sample1  |  487  | 174840 |
+| sample1  |  727  | 90679  |
+| sample1  |  729  | 13129  |
+| sample2  | 28132 | 147863 |
+| sample2  | 199   | 147545 |
+| sample2  | 729   | 131300 |
+
+#### :rocket: Command
+Let's run MeSS (using apptainer as the software deployment method) !
+```sh
+mess run -i samples.tsv --sdm apptainer 
+```
+#### :card_index_dividers: Outputs
+
+```sh
+📦mess_out
+ ┣ 📂assembly_finder
+ ┃ ┣ 📂download
+ ┃ ┃ ┣ 📂GCF_000144405.1
+ ┃ ┃ ┃ ┗ 📜GCF_000144405.1_ASM14440v1_genomic.fna.gz
+ ┃ ┃ ┣ 📂GCF_001298465.1
+ ┃ ┃ ┃ ┗ 📜GCF_001298465.1_ASM129846v1_genomic.fna.gz
+ ┃ ┃ ┣ 📂GCF_016127215.1
+ ┃ ┃ ┃ ┗ 📜GCF_016127215.1_ASM1612721v1_genomic.fna.gz
+ ┃ ┃ ┣ 📂GCF_020736045.1
+ ┃ ┃ ┃ ┗ 📜GCF_020736045.1_ASM2073604v1_genomic.fna.gz
+ ┃ ┃ ┣ 📂GCF_022869645.1
+ ┃ ┃ ┃ ┗ 📜GCF_022869645.1_ASM2286964v1_genomic.fna.gz
+ ┃ ┃ ┗ 📜.snakemake_timestamp
+ ┣ 📂fastq
+ ┃ ┣ 📜sample1_R1.fq.gz
+ ┃ ┣ 📜sample1_R2.fq.gz
+ ┃ ┣ 📜sample2_R1.fq.gz
+ ┃ ┗ 📜sample2_R2.fq.gz
+ ┣ 📜config.yaml
+ ┣ 📜coverages.tsv
+ ┗ 📜mess.log
+```
 
+Outputs described in more details [here](https://metagenlab.github.io/MeSS/guide/output/)
 
-#### Download and simulate
+#### :bar_chart: Resources usage
+Average resources usage measured 3 times with one CPU (within a [nextflow](https://github.com/nextflow-io/nextflow) process):
 
-Using the following file [minimal_test.tsv](https://github.com/metagenlab/MeSS/blob/main/mess/test_data/minimal_test.tsv)
+| task_id | hash      | native_id | name     | status    | exit | submit                  | duration | realtime | %cpu   | peak_rss | peak_vmem | rchar  | wchar  |
+| ------- | --------- | --------- | -------- | --------- | ---- | ----------------------- | -------- | -------- | ------ | -------- | --------- | ------ | ------ |
+| 1       | fe/03c2bc | 62286     | MESS (1) | COMPLETED | 0    | 2024-09-04 12:41:15.820 | 1m 50s   | 1m 50s   | 111.5% | 1.8 GB   | 9 GB      | 3.5 GB | 2.4 GB |
+| 1       | ff/0d03b1 | 73355     | MESS (1) | COMPLETED | 0    | 2024-09-04 12:55:12.903 | 1m 52s   | 1m 52s   | 112.6% | 1.7 GB   | 8.8 GB    | 3.5 GB | 2.4 GB |
+| 1       | 07/d352bf | 83576     | MESS (1) | COMPLETED | 0    | 2024-09-04 12:57:30.600 | 1m 50s   | 1m 50s   | 113.2% | 1.7 GB   | 8.9 GB    | 3.5 GB | 2.4 GB |
 
-```sh
-mess run -i minimal_test.tsv 
-```
+> On average, using `samples.tsv`, MeSS runs in under 2min, while using around 1.8GB of physical RAM
 
-#### Simulate from local fasta
+> [!NOTE]
+> Resources usage was measured exluding dependencies deployement time (conda env creation or container pulling)
 
-Download the [fasta directory](https://github.com/metagenlab/MeSS/tree/main/mess/test_data/fastas) and [table](https://github.com/metagenlab/MeSS/blob/main/mess/test_data/simulate_test.tsv)
+More details on resource usage in the [documentation](https://metagenlab.github.io/MeSS/benchmarks/resource-usage/)
 
-```sh
-mess simulate -i simulate_test.tsv --fasta fasta 
-```
 
 ## :sos: Help
 

mess/workflow/rules/preflight/functions.smk

@@ -69,16 +69,21 @@ fasta_cache = {}
 
 def fasta_input(wildcards):
     table = checkpoints.calculate_genome_coverages.get(**wildcards).output[0]
-    if table not in fasta_cache:
-        df = pd.read_csv(table, sep="\t", index_col="fasta")
-        fasta_cache[table] = df
-    df = fasta_cache[table]
-    return df.loc[wildcards.fasta]["path"]
+
+    df = pd.read_csv(table, sep="\t", index_col="fasta")
+    try:
+        return df.loc[wildcards.fasta]["path"].drop_duplicates()
+    except AttributeError:
+        return df.loc[wildcards.fasta]["path"]
+    # some samples use the same genome path, drop duplicates to avoid duplicate paths when processing fasta
 
 
 def list_fastas(wildcards):
     table = checkpoints.calculate_genome_coverages.get(**wildcards).output[0]
-    df = pd.read_csv(table, sep="\t")
+    if table not in fasta_cache:
+        df = pd.read_csv(table, sep="\t")
+        fasta_cache[table] = df
+    df = fasta_cache[table]
     fastas = list(set(df["fasta"]))
     return expand(os.path.join(dir.out.processing, "{fasta}.fasta"), fasta=fastas)
 

mess/workflow/scripts/samples.py

@@ -11,6 +11,8 @@
 dfs = []
 for file in files:
     df = pd.read_csv(file, sep="\t")
+    df.columns = df.columns.str.replace(" ", "")
+    df = df.map(lambda x: x.replace(" ", "") if isinstance(x, str) else x)
     dfs.append(df)
     try:
         samples = list(set(df["sample"]))