simplifying code and adding resources.yml #10
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@@ -4,13 +4,15 @@ build-backend = "setuptools.build_meta" | |
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| [tool.setuptools] | ||
| packages = ["qp_pacbio"] | ||
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| [tool.setuptools.package-data] | ||
| "qp_pacbio" = ["data/*"] | ||
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| [project] | ||
| name = "qp_pacbio" | ||
| # version strings must comply with PEP 440: | ||
| # https://peps.python.org/pep-0440/ | ||
| version = "2025.11" | ||
| authors = [{ name = "Qiita Development Team", email = "[email protected]" }] | ||
| description = "Qiita Plugin: PacBio Processing" | ||
| readme = "README.rst" | ||
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@@ -39,11 +41,15 @@ dependencies = [ | |
| 'pytest-cov', | ||
| 'numpy', | ||
| 'Jinja2', | ||
| 'PyYAML', | ||
| "qiita-files@https://github.com/qiita-spots/qiita-files/archive/master.zip", | ||
| "qiita_client@https://github.com/qiita-spots/qiita_client/archive/master.zip", | ||
| "woltka@git+https://github.com/qiyunzhu/woltka.git#egg=woltka", | ||
| "micov@git+https://github.com/biocore/micov.git#egg=micov", | ||
| ] | ||
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| [project.scripts] | ||
| configure_qp_pacbio = "qp_pacbio.scripts:config" | ||
| start_qp_pacbio = "qp_pacbio.scripts:execute" | ||
| finish_qp_pacbio = "qp_pacbio.scripts:finish_qp_pacbio" | ||
| biom_merge_pacbio = "qp_pacbio.scripts:biom_merge" | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,60 @@ | ||
| PacBio processing: | ||
| step-1: | ||
| node_count: 1 | ||
| nprocs: 16 | ||
| wall_time_limit: 1-00:00:00 | ||
| mem_in_gb: 200 | ||
| max_tasks: 16 | ||
| step-2: | ||
| node_count: 1 | ||
| nprocs: 1 | ||
| wall_time_limit: 00:10:00 | ||
| mem_in_gb: 2 | ||
| max_tasks: 16 | ||
| step-3: | ||
| node_count: 1 | ||
| nprocs: 8 | ||
| wall_time_limit: 01:00:00 | ||
| mem_in_gb: 10 | ||
| max_tasks: 16 | ||
| step-4: | ||
| node_count: 1 | ||
| nprocs: 8 | ||
| wall_time_limit: 01:00:00 | ||
| mem_in_gb: 6 | ||
| max_tasks: 16 | ||
| step-5: | ||
| node_count: 1 | ||
| nprocs: 8 | ||
| wall_time_limit: 00:30:00 | ||
| mem_in_gb: 2 | ||
| max_tasks: 16 | ||
| step-6: | ||
| node_count: 1 | ||
| nprocs: 8 | ||
| wall_time_limit: 00:30:00 | ||
| mem_in_gb: 2 | ||
| max_tasks: 16 | ||
| step-7: | ||
| node_count: 1 | ||
| nprocs: 8 | ||
| wall_time_limit: 01:00:00 | ||
| mem_in_gb: 50 | ||
| max_tasks: 16 | ||
| finish: | ||
| node_count: 1 | ||
| nprocs: 1 | ||
| wall_time_limit: 00:10:00 | ||
| mem_in_gb: 10 | ||
| Woltka v0.1.7, minimap2: | ||
| minimap2: | ||
| node_count: 1 | ||
| nprocs: 16 | ||
| wall_time_limit: 10:00:00 | ||
| mem_in_gb: 60 | ||
| max_tasks: 16 | ||
| merge: | ||
| node_count: 1 | ||
| nprocs: 16 | ||
| wall_time_limit: 1-00:00:00 | ||
| mem_in_gb: 120 |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,96 @@ | ||
| #!/bin/bash | ||
| #SBATCH -J {{job_name}} | ||
| #SBATCH -p qiita | ||
| #SBATCH -N {{node_count}} | ||
| #SBATCH -n {{nprocs}} | ||
| #SBATCH --time {{wall_time_limit}} | ||
| #SBATCH --mem {{mem_in_gb}}G | ||
| #SBATCH -o {{output}}/step-2/logs/%x-%A_%a.out | ||
| #SBATCH -e {{output}}/step-2/logs/%x-%A_%a.err | ||
| #SBATCH --array {{array_params}} | ||
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| source ~/.bashrc | ||
| set -e | ||
| {{conda_environment}} | ||
| cd {{output}}/step-1 | ||
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| step=${SLURM_ARRAY_TASK_ID} | ||
| input=$(head -n $step {{output}}/sample_list.txt | tail -n 1) | ||
| sample_name=`echo $input | awk '{print $1}'` | ||
| filename=`echo $input | awk '{print $2}'` | ||
| fn=`basename ${filename}` | ||
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| # updating the GUI when task 1 runs | ||
| if [[ "$step" == "1" ]]; then | ||
| python -c "from qp_pacbio.util import client_connect; qclient = client_connect('{{url}}'); qclient.update_job_step('{{qjid}}', 'Running step 2: ${SLURM_ARRAY_JOB_ID}')" | ||
| fi | ||
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| cat ${sample_name}.p_ctg.gfa | awk '$1=="S" && ($2 ~ /.c$/) {printf ">%s\n%s\n", $2, $3} ' > ../step-2/${sample_name}_circ.fa | ||
| seqkit split --by-id ../step-2/${sample_name}_circ.fa -O ../step-2/${sample_name}_split | ||
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| ### get all contigs for each sample | ||
| cat ${sample_name}.p_ctg.gfa | awk '$1=="S" {printf ">%s\n%s\n", $2, $3} ' > ../step-2/${sample_name}_all_contigs.fa | ||
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| cd ../step-2/${sample_name}_split | ||
| # making a copy of the small_LCG before they are removed | ||
| mkdir -p {{output}}/step-2/${sample_name}_small_LCG | ||
| find . -maxdepth 1 -type f -size -512k -print0 | xargs -0 -r cp -t ../${sample_name}_small_LCG | ||
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Collaborator
There was a problem hiding this comment. So small_LCG is defined by files in size < 512kb? Probably important to note in the documentation for the PacBio workflow.
Collaborator
There was a problem hiding this comment. I was expecting small_LCG to be defined by total genome size
Collaborator
There was a problem hiding this comment. by file size for now. Can be optimized, they are proportational to total genome size.
Collaborator
There was a problem hiding this comment. approximate 515,000 bases (half a million), because one character takes one byte approximately. |
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| ### remove small circular genomes | ||
| find . -type f -size -512k -exec rm -f {} + | ||
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| # this can result on not having any files left so | ||
| # making sure we have files left | ||
| # | ||
| # extract fasta id for all the genomes in the split folder | ||
| FILES=(*.fa) | ||
| if [ -f $FILES ]; then | ||
| for f in *.fa; do | ||
| k=${f##*/} | ||
| n=${f%.*} | ||
| grep -E "^>" $f >> circular_id.txt | ||
| done | ||
| sed -i 's/>//' circular_id.txt | ||
| seqkit grep -v -f circular_id.txt ../${sample_name}_all_contigs.fa > ../${sample_name}_noLCG.fa | ||
| else | ||
| cp ../${sample_name}_all_contigs.fa ../${sample_name}_noLCG.fa | ||
| fi | ||
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| lcg_folder={{result_fp}}/${sample_name}/LCG/ | ||
| mkdir -p ${lcg_folder} | ||
| FILES=({{output}}/step-2/${sample_name}_split/*.fa) | ||
| if [ -f $FILES ]; then | ||
| for f in `ls {{output}}/step-2/${sample_name}_split/*.fa`; do | ||
| sn=`basename ${f/_circ/}`; | ||
| sn=${sn/part_/}; | ||
| cat $f | gzip > ${lcg_folder}/${sn/.fa/.fna}.gz; | ||
| done | ||
| fi | ||
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| mkdir -p {{result_fp}}/${sample_name}/ | ||
| if [ -f {{output}}/step-2/${sample_name}_noLCG.fa ]; then | ||
| cat {{output}}/step-2/${sample_name}_noLCG.fa | gzip > {{result_fp}}/${sample_name}/${sample_name}.noLCG.fna.gz | ||
| fi | ||
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| touch {{output}}/step-2/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| # if the files don't exist, it means that this step didn't generate any | ||
| # inputs for the next step; thus generating all the completed files | ||
| if [[ ! -f "$FILES" && ! -f "{{output}}/step-2/${sample_name}_noLCG.fa" ]]; then | ||
| touch {{output}}/step-3/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| touch {{output}}/step-4/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| touch {{output}}/step-5/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| touch {{output}}/step-6/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| touch {{output}}/step-7/completed_${SLURM_ARRAY_TASK_ID}.log | ||
| fi | ||
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| # saving small LCG, note that these are not processed downstrem so not | ||
| # relevant to the "complete" files | ||
| small_lcg_folder={{result_fp}}/${sample_name}/small_LCG/ | ||
| mkdir -p ${small_lcg_folder} | ||
| FILES=({{output}}/step-2/${sample_name}_small_LCG/*.fa) | ||
| if [ -f $FILES ]; then | ||
| for f in `ls {{output}}/step-2/${sample_name}_small_LCG/*.fa`; do | ||
| sn=`basename ${f/_circ/}`; | ||
| sn=${sn/part_/}; | ||
| cat $f | gzip > ${small_lcg_folder}/${sn/.fa/.fna}.gz; | ||
| done | ||
| fi | ||
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Minor but you can probably get micov from
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It would be nice if we can install from bioconda