Metagenomic Sequence Simulator (MeSS)

The Metagenomic Sequence Simulator (MeSS) is a Snakemake pipeline, implemented using Snaketool, for simulating illumina, Oxford Nanopore (ONT) and Pacific Bioscience (PacBio) shotgun metagenomic samples.

: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.

```mermaid %%{init: {'theme':'forest'}}%% flowchart LR input["samples.tsv or samples/*.tsv"] --> taxons

subgraph genomedownload["genome download"] dlchoice{download ?} taxons["taxons or accesions"] --> dlchoice dlchoice -->|yes| assemblyfinder dlchoice -->|no| fasta assemblyfinder --> fasta end style genomedownload color:#15161a

input --> distchoice subgraph communitydesign["**community design**"] distchoice{draw distribution ?} distchoice -->|yes| dist["distribution (lognormal, even)"] dist --> abundances distchoice -->|no| reads distchoice -->|no| bases distchoice -->|no| abundances depth["coverage depth"] reads --> depth bases --> depth abundances["abundances (sequence, taxonomic)"] --> depth end style communitydesign color:#15161a style community_design color:#15161a

fasta --> simulator depth --> simulator

simulator["read simulator (art_illumina, pbsim3...)"] simulator --> bam simulator --> fastq simulator --> CAMI-profile

%% subgraph color fills classDef red fill:#faeaea,color:#fff,stroke:#333; classDef blue fill:#eaecfa,color:#fff,stroke:#333; class genome_download blue

class community_design red ```

:books: Documentation

More details can be found in the documentation

:zap: Quick start

:gear: Installation

• Conda (Miniforge)

sh conda create -n mess mess

• Docker

sh docker pull ghcr.io/metagenlab/mess:latest

• From source

sh git clone https://github.com/metagenlab/MeSS.git pip install -e MeSS

:pagefacingup: 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

sh mess run -i samples.tsv

[!IMPORTANT] Apptainer is the default and recommended dependency deployment method for maximum reproducibility !

If you would like to use conda you can specify --sdm conda.

:cardindexdividers: Outputs

• Downloaded genomes in mess_out/assembly_finder/download

sh ┣ 📂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

• Simulated reads in mess_out/fastq

sh ┣ 📜sample1_R1.fq.gz ┣ 📜sample1_R2.fq.gz ┣ 📜sample2_R1.fq.gz ┗ 📜sample2_R2.fq.gz

[!TIP] By default mess outputs paired illumina reads with the Hiseq25k error profile. Other outputs, and error profiles are described here and here

:bar_chart: Resources usage

Using samples.tsv, mess runs in under 2min, while using around 1.8GB of physical RAM

| taskid | hash | nativeid | name | status | exit | submit | duration | realtime | %cpu | peakrss | peakvmem | 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 |

[!NOTE] Average resources usage measured 3 times with one CPU (using nextflow, excluding dependency deployment time).

More details in the resource usage documentation

:fire: Features

Using phage.tsv

| sample | taxon | cov_sim | | :----- | :----- | :------ | | phage | 347329 | 200 |

:dna: Multi sequencing technology

• Illumina

sh mess run -i phage.tsv --tech illumina -o mess_out/illumina seqkit stats --all -T -b mess_out/illumina/fastq/*

| file | numseqs | sumlen | avglen | N50 | Q20(%) | Q30(%) | AvgQual | | :------------- | :------- | :------ | :------ | :-- | :----- | :----- | :------ | | phageR1.fq.gz | 44000 | 6600000 | 150.0 | 150 | 98.01 | 91.67 | 27.81 | | phage_R2.fq.gz | 44000 | 6600000 | 150.0 | 150 | 97.31 | 89.65 | 26.52 |

• Nanopore

sh mess run -i phage.tsv --tech nanopore -o mess_out/nanopore seqkit stats --all -T -b mess_out/nanopore/fastq/*

| file | numseqs | sumlen | avg_len | N50 | Q20(%) | Q30(%) | AvgQual | | :---------- | :------- | :------- | :------ | :---- | :----- | :----- | :------ | | phage.fq.gz | 1486 | 13203006 | 8884.9 | 12329 | 73.99 | 62.65 | 13.60 |

• PacBio HiFi

sh mess run -i phage.tsv -o mess_out/pacbio --tech pacbio --error hifi seqkit stats --all -T -b mess_out/pacbio/fastq/*

| file | numseqs | sumlen | avg_len | N50 | Q20(%) | Q30(%) | AvgQual | | :---------- | :------- | :------- | :------ | :---- | :----- | :----- | :------ | | phage.fq.gz | 1430 | 12588621 | 8803.2 | 12666 | 99.92 | 99.78 | 40.51 |

[!NOTE] We use pbsim3 to simulate multi-pass CLR reads which are converted to HiFi reads with ccs.

PacBio HiFi reads simulations usually take longer compared to other error profiles.

:o: Circular assemblies

Inspired by readSimulator's approach, mess can shuffle genome start points to get circular genome assemblies.

[!WARNING] All contigs in the fasta will be circularised

• Linear (default, --rotate 1)

sh mess run -i phage.tsv -o mess_out/linear

• Circular (--rotate 3)

sh mess run -i phage.tsv --rotate 3 -o mess_out/circular

[!NOTE] Assembled using unicycler, visualized using bandage

:sos: Help

All command-line options at described here

Citation

Please consider citing MeSS if you use it in your work.

Farid Chaabane, Trestan Pillonel, Claire Bertelli, MeSS and assembly_finder: A toolkit for in silico metagenomic sample generation, Bioinformatics, 2024;, btae760, https://doi.org/10.1093/bioinformatics/btae760

BibTeX @article{chaabane_mess_2024, title = {MeSS and assembly_finder: A toolkit for in silico metagenomic sample generation}, issn = {1367-4811}, url = {https://doi.org/10.1093/bioinformatics/btae760}, doi = {10.1093/bioinformatics/btae760}, journal = {Bioinformatics}, author = {Chaabane, Farid and Pillonel, Trestan and Bertelli, Claire}, month = dec, year = {2024}, pages = {btae760}, }