A battery included snakemake pipeline to run bacterial GWAS on a set of assembled genomes. Multiple phenotypes can be defined and used for the associations.

For each phenotype, the pipeline will run associations using 5 sets of genetic variants:

• individual unitigs
• gene presence/absence
• rare variants (i.e. gene burden test)
• gene cluster specific k-mers
• all unitigs combined (i.e. whole genome machine learning model)

For each set of genetic variants a number of downstream analyses will be run to provide annotated results and functional enrichments.

It will additionally compute the heritability of each phenotype using the lineage information and the whole set of unitigs.

It can also optionally compute a phylogenetic tree of all samples.

Make a copy of this repository (optionally you can first create a new repository by clicking on the "Use this template" button from Github's interface), including the submodules:

git clone --recursive https://github.com/microbial-pangenomes-lab/microGWAS.git microGWAS
cd microGWAS

Create and activate a conda environment to run the bootstrapping script and the pipeline (named microGWAS, can be skipped if it's already present):

conda env create -f environment.yml
conda activate microGWAS

Save your phenotype table as a tab-separated file as data/data.tsv. The first column should contain the sample names, then there should be two columns listing the relative or absolute path to the assemblies (fasta, SAMPLE.fasta) and annotations (gff, SAMPLE.gff), and subsequent columns should contain the target phenotype(s).

Additional columns are allowed and will be simply ignored. An example file can be found in the test directory (test/data.tsv).

This is how it should look like:

strain	fasta	gff	killed	phenotype	covariate1	covariate2
ECOR-01	test/small_fastas/ECOR-01.fasta	test/small_gffs/ECOR-01.gff	0	0	0.20035297602710966	1
ECOR-02	test/small_fastas/ECOR-02.fasta	test/small_gffs/ECOR-02.gff	10	1	0.8798471273587852	1
ECOR-03	test/small_fastas/ECOR-03.fasta	test/small_gffs/ECOR-03.gff	0	0	0.008404161045130532	0
ECOR-04	test/small_fastas/ECOR-04.fasta	test/small_gffs/ECOR-04.gff	0	0	0.04728873355931962	1

Edit the params section (##### params #####) of the config/config.yaml file (at the top).

• targets: Name of the columns in the phenotypes file to be used in the associations. In the example below the target phenotype will be the one considered to test for the associations. phenotype2 is commented (# in front) and will simply be ignored.

targets: [
         "phenotype"
         #"phenotype2",
         ]

• covariates: Covariates to be used for the associations for each phenotype. THe numbers refer to the columns in the phenotype file that should be used as covariates. The suffix "q" is added when they are quantitative and not binary. The column numering is 1-based. See also: https://pyseer.readthedocs.io/en/master/usage.html#phenotype-and-covariates for more information. In the example below, the columns 6 and 7 are used for the target phenotype. The column 6 contains a quantitative covariate. The phenotype2 is commented and will simply be ignored.

covariates:
        phenotype: "--use-covariates 6q 7"
#        phenotype2: "--use-covariates 7",

• MLST scheme: Change the mlst scheme to be used to compute lineages. Find more information on the available schemes: https://github.com/tseemann/mlst?tab=readme-ov-file#available-schemes
• references for association summaries and annotation: Provide the name of the references to be used for annotation of hits. Multiple strains can be provided, but only one strain can be specified to be used as a reference for the enrichment analyses. For convenience the defaults for E. coli are placed as defaults, and those for P. aeruginosa are commented.
• species_amr: species to be used for AMR and virulence predictions
• lineages_file: lineage file to use. By default the mlst lineages are used, but you can specify your custom lineages list.
• eggnogdb: Tax ID of eggnog database to download. By default, there is the Bacteria (2). Available tax IDs can be found at http://eggnog5.embl.de/#/app/downloads
• filters to remove spurious hits: all defaults are set. Change them to be more/less stringent
  • length: Minimum unitig length (ignored if --panfeed is used)
  • min_hits: Minimum number of strains
  • max_genes: Maximum number of genes to which a unitig/kmer can map

If you already have the eggnog-db downloaded, create a symbolic link to the directory in which the databases for eggnog-mapper have been placed:

ln -s /fast-storage/miniconda3/envs/eggnog-mapper/lib/python3.9/site-packages/data/ data/eggnog-mapper

If you do not have the eggnog-db downloaded, it will be automatically downloaded within the pipeline.

The bootstrapping script populates the input files for the pipeline and downloads the reference genomes used for annotation of hits and the rare variants analyses. The bootstrap.sh script takes multiple arguments:

• Genus: Genus of the species under study (e.g. Escherichia)
• Species: Species of the species under study (e.g. coli)
• Reference: Strain name for the reference to be used for rare variants (e.g. IAI39, name should be the one NCBI uses)
• Assemblies: Comma separated list of NCBI assembly IDs to be downloaded as references (e.g. GCF_000013305.1,GCF_000007445.1,GCF_000026305.1,GCF_000026265.1)

To run the boostrapping, activate the microGWAS environment and then run the bootstrap.sh. Here we provide some examples for E. coli and P. aeruginosa.

The following example works for E. coli (and downloads the references listed by default in config/config.yaml):

bash bootstrap.sh Escherichia coli IAI39 GCF_000013305.1,GCF_000007445.1,GCF_000026305.1,GCF_000026265.1,GCF_000026345.1,GCF_000005845.2,GCF_000026325.1,GCF_000013265.1 

And the following works for P. aeruginosa (and matches the references commented in config/config.yaml):

bash bootstrap.sh Pseudomonas aeruginosa UCBPP-PA14 GCF_000006765.1,GCF_000014625.1 

The following example runs all the analyses using 24 cores and mamba as the conda backend to install each environment:

snakemake -p annotate_summary find_amr_vag map_back manhattan_plots heritability enrichment_plots qq_plots tree --cores 24 --verbose --use-conda --conda-frontend mamba

If you want to run only the GWAS, without generating a phylogenetic tree and predicting antimicrobial resistance and virulence associated genes, you can use the following example instead, which uses "vanilla" conda:

snakemake -p annotate_summary map_back manhattan_plots heritability enrichment_plots qq_plots --cores 24 --verbose --use-conda

or using mamba as the conda backend:

snakemake -p annotate_summary map_back manhattan_plots heritability enrichment_plots qq_plots --cores 24 --verbose --use-conda --conda-frontend mamba

We have included a small dataset in order to test the pipeline in reasonable time and resources. In its current state continuous integration (CI) in the cloud is not feasible because certain rules require significant time and resources to complete (annotate_reference, get_snps). Some workarounds might be added in the future to bypass those rules. In the meantime the tests can be run on a decent laptop with 8 cores and at least ~10Gb RAM in a few hours.

The test dataset has been created from that used in a mouse model of bloodstream infection.

To run the tests, do the following:

cd test
bash run_tests.sh

The script will prepare the input files, run the bootstrapping script, then run snakemake twice, first in "dry" mode, and then "for real".

Please note that the only rule that is not tested is the one estimating lineages (lineage_st), as the test dataset is a reduced part of the E. coli genome.

• Add the ability to use covariates in the associations
• manhattan_plot.py : handle cases in which the reference genome has more than one chromosome (either because it has plasmids or because it is a draft genome) (enhancement issue)
• Easily switch to poppunk for lineage computation
• Combine all annotations in a series of webpages (enhancement issue)
• Add references to the tools used in the pipeline (documentation issue)
• Use random file names for things that go in /tmp to avoid conflicts
• Use /tmp directories (as implemented by snakemake) to be efficient in I/O heavy rules
• Delete everything but the necessary files upon completion of a rule
  • snippy
  • panaroo
• Avoid rules that list each input file as it might eventually become too long (including the current use of the data/fastas and data/gffs directories)
• Run QC on phenotypic data/genomes as part of bootstrapping
• Use snakemake resources system to budget memory requirements (enhancement issue)
• Swap sift4g for more modern alternatives (enhancement issue)
• Create a minimal test set that yields some hits and runs in reasonable time (~300 Ecoli dataset from BSI mouse model)
• Also generate panfeed plots, and annotate its results
• Harden panfeed rules against stochastic gzip file corruption (fixed here)
• Swap unitig-counter for bifrost or cuttlefish (enhancement issue)
• Heritability estimates using different distributions (i.e. for binary phenotypes the normal distribution is likely not appropriate?)
• Add abritamr to detect known AMR/VAGs - necessary for this pipeline???
• Add txt file that describes outputs produced from running the pipeline (in progress)
• Add documentation using something like read the docs
• Add script to check for duplicated contigs during the bootstrap

Burgaya, J., Damaris, B. F., Fiebig, J., & Galardini, M. (2024). microGWAS: A computational pipeline to perform large scale bacterial genome-wide association studies (p. 2024.07.08.602456). bioRxiv. https://doi.org/10.1101/2024.07.08.602456

• unitig-counter: 10.1371/journal.pgen.1007758 and 10.1128/mbio.01344-20
• mlst: https://doi.org/10.12688/wellcomeopenres.14826.1
• abritamr: 10.1038/s41467-022-35713-4
• mash: 10.1186/s13059-016-0997-x
• panaroo: 10.1186/s13059-020-02090-4
• fasttree: 10.1371/journal.pone.0009490
• snp-sites: 10.1099/mgen.0.000056
• snippy: https://github.com/tseemann/snippy
• bcftools: 10.1093/gigascience/giab008
• limix: 10.1101/003905
• ALBI: 10.1016/j.ajhg.2016.04.016
• sequence_unet: 10.1186/s13059-023-02948-3
• pyseer: 10.1093/bioinformatics/bty539 and 10.1101/852426
• bwa: arXiv:1303.3997
• bedtools: 10.1093/bioinformatics/btq033 and 10.1093/bioinformatics/btr539
• eggnog-mapper: 10.1093/molbev/msx148
• panfeed: 10.1099/mgen.0.001129

Marco Galardini, Judit Burgaya, Bamu F. Damaris, Jenny Fiebig