FASTQC

Generate fastq qc statistics using fastqc.

URL: https://github.com/s-andrews/FastQC

Example

This wrapper can be used in the following way:

rule fastqc:
    input:
        "reads/{sample}.fastq",
    output:
        html="qc/fastqc/{sample}.html",
        zip="qc/fastqc/{sample}_fastqc.zip", # the suffix _fastqc.zip is necessary for multiqc to find the file. If not using multiqc, you are free to choose an arbitrary filename
    params:
        extra="--quiet",
        mem_overhead_factor=0.1,
    log:
        "logs/fastqc/{sample}.log",
    threads: 1
    resources:
        mem_mb = 1024,
    wrapper:
        "v7.6.0/bio/fastqc"

Note that input, output and log file paths can be chosen freely.

When running with

snakemake --use-conda

the software dependencies will be automatically deployed into an isolated environment before execution.

Software dependencies

• fastqc=0.12.1

• snakemake-wrapper-utils=0.8.0

Input/Output

Input:

• fastq file

Output:

• html: statistics file in HTML format

• zip: statistics in text format (zip’ed)

Params

• extra: additional program arguments

• mem_overhead_factor: factor of total memory saved for overhead

Authors

• Julian de Ruiter

Code

"""Snakemake wrapper for fastqc."""

__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
from snakemake_wrapper_utils.snakemake import get_mem

extra = snakemake.params.get("extra", "")
log = snakemake.log_fmt_shell(stdout=True, stderr=True)
# Define memory per thread (https://github.com/s-andrews/FastQC/blob/master/fastqc#L201-L222)
mem_overhead_factor = snakemake.params.get("mem_overhead_factor", 0.1)
assert (
    0 <= mem_overhead_factor < 1
), f"mem_overhead_factor must be between 0 and 1, got {mem_overhead_factor}"
mem_per_thread_mb = int(
    get_mem(snakemake, "MiB") / snakemake.threads * (1.0 - mem_overhead_factor)
)


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


# If you have multiple input files fastqc doesn't know what to do. Taking silently only first gives unapreciated results

if len(snakemake.input) > 1:
    raise IOError("Got multiple input files, I don't know how to process them!")

# 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"
        " --threads {snakemake.threads}"
        " --memory {mem_per_thread_mb}"
        " {extra}"
        " --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}")