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Snakefile
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Snakefile
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localrules:
all,
filter_significant,
rule all:
input:
"data/figures/manhattan_plot.pdf",
rule gubbins:
input:
"data/gwas/{abx}/alignments/gc_{abx}_pseudogenomes.fasta",
output:
"data/gwas/{abx}/gubbins/gc_{abx}.final_tree.tre",
params:
directory="data/gwas/{abx}/gubbins/"
resources:
cpus=12,
mem_mb=lambda wildcards, attempt: attempt * 16000,
time=lambda wildcards, attempt: attempt * 8000,
conda:
"conda_envs/gubbins.yml"
shell:
"""
mkdir -p {params.directory}
run_gubbins.py --threads {resources.cpus} --prefix data/gwas/{abx}/gubbins/gc_{abx} {input}
"""
rule unitigs:
input:
strain_list="data/gwas/{abx}/unitigs/strain_list.txt",
output:
"data/gwas/{abx}/unitigs/unitigs/unitigs.txt",
params:
directory="data/gwas/{abx}/unitigs/unitigs/",
resources:
cpus=4,
mem_mb=lambda wildcards, attempt: attempt * 10000,
time=lambda wildcards, attempt: attempt * 480,
conda:
"conda_envs/unitig-counter.yml"
shell:
"""
unitig-counter -strains {input.strain_list} -output unitigs/ -nb-cores {resources.cpus}
mv unitigs/* {params.directory}
rmdir unitigs/
"""
rule kmers_tet:
input:
"data/gwas/tet/unitigs/strain_list.txt"
output:
"data/gwas/tet/kmers/fsm_kmers.txt.gz",
resources:
cpus=1,
mem_mb=lambda wildcards, attempt: attempt * 50000,
time=lambda wildcards, attempt: attempt * 480,
shell:
"""
software/fsm-lite/fsm-lite -l {input} -s 34 -S 3419 -v -t fsm_kmers | gzip -c - > {output}
"""
rule kmers_pcn:
input:
"data/gwas/pcn/unitigs/strain_list.txt"
output:
"data/gwas/pcn/kmers/fsm_kmers.txt.gz",
resources:
cpus=1,
mem_mb=lambda wildcards, attempt: attempt * 200000,
time=lambda wildcards, attempt: attempt * 2160,
shell:
"""
software/fsm-lite/fsm-lite -l {input} -s 62 -S 6158 -v -t fsm_kmers | gzip -c - > {output}
"""
rule similarity_matrix:
input:
tree="data/gwas/{abx}/gubbins/gc_{abx}.final_tree.tre",
output:
matrix="data/gwas/{abx}/gubbins/similarity_matrix.txt",
resources:
cpus=1,
mem_mb=lambda wildcards, attempt: attempt * 10000,
time=lambda wildcards, attempt: attempt * 30,
conda:
"conda_envs/pyseer.yml"
shell:
"python software/pyseer/scripts/phylogeny_distance.py --lmm {input.tree} > {output.matrix}"
rule lmm_gwas:
input:
pheno="data/gwas/{abx}/{abx}_mics.tsv",
unitigs="data/gwas/{abx}/unitigs/unitigs/unitigs.txt",
similarity="data/gwas/{abx}/gubbins/similarity_matrix.txt",
metadata="data/gwas/{abx}/{abx}_metadata.tsv"
output:
patterns="data/gwas/{abx}/unitig_patterns.txt",
significance="data/gwas/{abx}/{abx}_unitig_significance.txt",
params:
pheno_column="{abx}_log2"
resources:
cpus=1,
mem_mb=lambda wildcards, attempt: attempt * 10000,
time=lambda wildcards, attempt: attempt * 360,
conda:
"conda_envs/pyseer.yml"
shell:
"""
pyseer --lmm --phenotypes {input.pheno} --similarity {input.similarity} --uncompressed --kmers {input.unitigs} --phenotype-column {params.pheno_column} --covariates {input.metadata} --use-covariates 2 3 4 --output-patterns {output.patterns} --cpu 1 > {output.significance}
"""
rule lmm_gwas_kmers:
input:
pheno="data/gwas/{abx}/{abx}_mics.tsv",
kmers="data/gwas/{abx}/kmers/fsm_kmers.txt.gz",
similarity="data/gwas/{abx}/gubbins/similarity_matrix.txt",
metadata="data/gwas/{abx}/{abx}_metadata.tsv"
output:
patterns="data/gwas/{abx}/kmer_patterns.txt",
significance="data/gwas/{abx}/{abx}_kmer_significance.txt",
params:
pheno_column="{abx}_log2"
resources:
cpus=1,
mem_mb=lambda wildcards, attempt: attempt * 10000,
time=lambda wildcards, attempt: attempt * 1440,
conda:
"conda_envs/pyseer.yml"
shell:
"""
pyseer --lmm --phenotypes {input.pheno} --similarity {input.similarity} --kmers {input.kmers} --phenotype-column {params.pheno_column} --covariates {input.metadata} --use-covariates 2 3 4 --output-patterns {output.patterns} --cpu 1 > {output.significance}
"""
rule pyseer_count_patterns:
input:
"data/gwas/{abx}/unitig_patterns.txt",
output:
"data/gwas/{abx}/significance_limits.txt",
resources:
cpus=1,
mem_mb=1000,
time=10,
conda:
"conda_envs/pyseer.yml"
shell:
"""
python software/pyseer/scripts/count_patterns.py {input} > {output}
"""
rule pyseer_count_patterns_kmers:
input:
"data/gwas/{abx}/kmer_patterns.txt",
output:
"data/gwas/{abx}/kmer_significance_limits.txt",
resources:
cpus=1,
mem_mb=1000,
time=10,
conda:
"conda_envs/pyseer.yml"
shell:
"""
python software/pyseer/scripts/count_patterns.py {input} > {output}
"""
rule filter_significant:
input:
script="scripts/filter_significant_unitigs.R",
limit="data/gwas/{abx}/significance_limits.txt",
unitig_significance="data/gwas/{abx}/{abx}_unitig_significance.txt",
output:
"data/gwas/{abx}/{abx}_unitig_significance_filtered.txt",
shell:
"""
load_R
load_R_packages
Rscript scripts/filter_significant_unitigs.R {input.limit} {input.unitig_significance} {output}
"""
rule filter_significant_kmers:
input:
script="scripts/filter_significant_unitigs.R",
limit="data/gwas/{abx}/kmer_significance_limits.txt",
unitig_significance="data/gwas/{abx}/{abx}_kmer_significance.txt",
output:
"data/gwas/{abx}/{abx}_kmer_significance_filtered.txt",
shell:
"""
load_R
load_R_packages
Rscript scripts/filter_significant_unitigs.R {input.limit} {input.unitig_significance} {output}
"""
rule annotate_unitigs:
input:
unitig_significance="data/gwas/{abx}/{abx}_unitig_significance.txt",
reference="data/gwas/references.txt"
output:
"data/gwas/{abx}/{abx}_unitig_annotated_WHO_N.txt",
resources:
cpus=1,
mem_mb=1000,
time=10
conda:
"conda_envs/pyseer.yml"
shell:
"""
annotate_hits_pyseer {input.unitig_significance} {input.reference} {output}
"""
rule annotate_kmers:
input:
kmer_significance="data/gwas/{abx}/{abx}_kmer_significance.txt",
reference="data/gwas/references.txt"
output:
"data/gwas/{abx}/{abx}_kmer_annotated_WHO_N.txt",
resources:
cpus=1,
mem_mb=10000,
time=500
conda:
"conda_envs/pyseer.yml"
shadow: "shallow"
params:
out_dir="data/gwas/{abx}/"
shell:
"""
annotate_hits_pyseer {input.kmer_significance} {input.reference} {output}
mv remaining_kmers.fa {params.out_dir}
mv remaining_kmers.txt {params.out_dir}
"""
rule manhattan_plot:
input:
expand("data/gwas/{abx}/{abx}_unitig_annotated_WHO_N.txt", abx=["pcn","tet"]),
expand("data/gwas/{abx}/significance_limits.txt", abx=["pcn", "tet"])
output:
"data/figures/manhattan_plot.pdf"
shell:
"""
load_R
load_R_packages
Rscript manhattan_plot.R
"""