Second workflow that takes the output of the guppy workflow to run fastqc and create a multiQC report. Then run Mothur for analysis.
This workflow takes the output from the GUPPY workflow and runs fastqc, multiqc, and mothur.
Authors
-
Hans Vasquez-Gross
-
Lucas Bishop
Usage
Simple
Step 1: Install workflow
clone this workflow to your local computer
Step 2: Configure workflow
Configure the workflow according to your needs by editing the config.yaml to configure your input basespace PROJECT directory.
Step 3: Execute workflow
Test your configuration by performing a dry-run via
snakemake --use-conda -n
Code Snippets
51 52 | shell: "ln -s {input} {output}" |
65 66 | wrapper: "v1.3.2/bio/fastqc" |
79 80 | wrapper: "v1.3.2/bio/multiqc" |
99 100 101 102 103 | shell: """ cd {params.indir} mothur "#set.dir(output={params.outdir}); fastq.info(fastq={params.fq})" """ |
122 123 124 125 126 | shell: """ cd {params.indir} mothur "#trim.seqs(fasta={params.fasta}, qfile={params.qual}, qaverage=10, processors=16)" """ |
145 146 147 148 149 150 151 152 | shell: """ cd {params.mothurdir} touch {output.finished} mothur "#set.dir(output={params.workingdir}); merge.files(input={params.fasta}, output=merged_results.fasta); make.group(fasta={params.fasta}, groups={params.groups})" || true """ |
179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | shell: """ cd {params.mothurdir} touch {output.finished} mothur "#set.dir(output={params.workingdir}); merge.files(input={params.fasta}, output=merged_results.fasta); make.group(fasta={params.fasta}, groups={params.groups}); screen.seqs(fasta=merged_results.fasta, group=current, maxambig=0, maxlength=1700, maxhomop=8); unique.seqs(fasta=current); count.seqs(name=current, group=current); align.seqs(fasta=current, reference={input.refbac}); filter.seqs(fasta=current, vertical=T); unique.seqs(fasta=current, count=current); pre.cluster(fasta=current, count=current, diffs=2); chimera.vsearch(fasta=current, count=current, dereplicate=T); remove.seqs(fasta=current, accnos=current); classify.seqs(fasta=current, count=current, reference={input.trainsetfasta}, taxonomy={input.trainsettax}, cutoff=80); remove.lineage(fasta=current, count=current, taxonomy=current, taxon={params.lineageremove}); phylotype(taxonomy=current); make.shared(list=current, count=current, label=1); classify.otu(list=current, count=current, taxonomy=current, label=1)" || true """ |
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | __author__ = "Julian de Ruiter" __copyright__ = "Copyright 2017, Julian de Ruiter" __email__ = "julianderuiter@gmail.com" __license__ = "MIT" from os import path import re from tempfile import TemporaryDirectory from snakemake.shell import shell log = snakemake.log_fmt_shell(stdout=True, stderr=True) def basename_without_ext(file_path): """Returns basename of file path, without the file extension.""" base = path.basename(file_path) # Remove file extension(s) (similar to the internal fastqc approach) base = re.sub("\\.gz$", "", base) base = re.sub("\\.bz2$", "", base) base = re.sub("\\.txt$", "", base) base = re.sub("\\.fastq$", "", base) base = re.sub("\\.fq$", "", base) base = re.sub("\\.sam$", "", base) base = re.sub("\\.bam$", "", base) return base # Run fastqc, since there can be race conditions if multiple jobs # use the same fastqc dir, we create a temp dir. with TemporaryDirectory() as tempdir: shell( "fastqc {snakemake.params} -t {snakemake.threads} " "--outdir {tempdir:q} {snakemake.input[0]:q}" " {log}" ) # Move outputs into proper position. output_base = basename_without_ext(snakemake.input[0]) html_path = path.join(tempdir, output_base + "_fastqc.html") zip_path = path.join(tempdir, output_base + "_fastqc.zip") if snakemake.output.html != html_path: shell("mv {html_path:q} {snakemake.output.html:q}") if snakemake.output.zip != zip_path: shell("mv {zip_path:q} {snakemake.output.zip:q}") |
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | __author__ = "Julian de Ruiter" __copyright__ = "Copyright 2017, Julian de Ruiter" __email__ = "julianderuiter@gmail.com" __license__ = "MIT" from os import path from snakemake.shell import shell input_dirs = set(path.dirname(fp) for fp in snakemake.input) output_dir = path.dirname(snakemake.output[0]) output_name = path.basename(snakemake.output[0]) log = snakemake.log_fmt_shell(stdout=True, stderr=True) shell( "multiqc" " {snakemake.params}" " --force" " -o {output_dir}" " -n {output_name}" " {input_dirs}" " {log}" ) |
Support
Do you know this workflow well? If so, you can
request seller status , and start supporting this workflow.
Created: 1yr ago
Updated: 1yr ago
Maitainers:
public
URL:
https://github.com/Nevada-Bioinformatics-Center/snakemake_fastqc_mothur
Name:
snakemake_fastqc_mothur
Version:
1
Downloaded:
0
Copyright:
Public Domain
License:
MIT License
Keywords:
- Future updates
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