BioWDL: small-RNA

This pipeline can be used to process small RNAs (with a transcipt length of 20-60bp) starting from FastQ files. It will perform quality control (using FastQC and MultiQC), adapter clipping (using cutadapt), mapping (using bowtie) and expression quantification (using HTSeq-Count).

This pipeline is part of BioWDL developed by the SASC team at Leiden University Medical Center.

Usage

This pipeline can be run using Cromwell:

java -jar cromwell-<version>.jar run -i inputs.json small-rna.wdl

Dependency requirements and tool versions

Biowdl pipelines use docker images to ensure reproducibility. This means that biowdl pipelines will run on any system that has docker installed. Alternatively they can be run with singularity.

For more advanced configuration of docker or singularity please check the cromwell documentation on containers.

Images from biocontainers are preferred for biowdl pipelines. The list of default images for this pipeline can be found in the default for the dockerImages input.

Inputs

Inputs are provided through a JSON file. The minimally required inputs are described below, but additional inputs are available. A template containing all possible inputs can be generated using Womtool as described in the WOMtool documentation.

{
  "SmallRna.bowtieIndexFiles": "A list of all files that belong to the bowtie index",
  "SmallRna.gtfFiles": "A list of structs containing the GTF Files and information about ID and feature attributes",
  "SmallRna.dockerImagesFile": "A file containing all the docker images used in the workflow. A default is provided as 'dockerImages.yml'.",
  "SmallRna.sampleConfigFile": "The sample configuration file. See below for more details.",
  "SmallRna.stranded": "The input for the stranded parameter of HtSeqCount. Default: 'no'"
}

Sample configuration

Verification

All samplesheet formats can be verified using biowdl-input-converter. It can be installed with pip install biowdl-input-converter or conda install biowdl-input-converter (from the bioconda channel). Python 3.7 or higher is required.

With biowdl-input-converter --validate samplesheet.csv The file “samplesheet.csv” will be checked. Also the presence of all files in the samplesheet will be checked to ensure no typos were made. For more information check out the biowdl-input-converter readthedocs page.

CSV Format

The sample configuration can be given as a csv file with the following columns: sample, library, readgroup, R1, R1_md5, R2, R2_md5.

The easiest way to create a samplesheet is to use a spreadsheet program such as LibreOffice Calc or Microsoft Excel, and create a table:

NOTE: R1_md5, R2 and R2_md5 are optional do not have to be filled. And additional fields may be added (eg. for documentation purposes), these will be ignored by the pipeline.

After creating the table in a spreadsheet program it can be saved in csv format.

YAML format

The sample configuration can also be a YML file which adheres to the following structure:

samples:
  - id: <sampleId>
    libraries:
      - id: <libId>
        readgroups:
          - id: <rgId>
            reads:
              R1: <Path to first-end FastQ file.>
              R1_md5: <MD5 checksum of first-end FastQ file.>
              R2: <Path to second-end FastQ file.>
              R2_md5: <MD5 checksum of second-end FastQ file.>

Replace the text between < > with appropriate values. MD5 values may be omitted and R2 values may be omitted in the case of single-end data. Multiple readgroups can be added per library and multiple libraries may be given per sample.

Example

The following is an example of what an inputs JSON might look like:

{
  "SmallRna.sampleConfigFile": "/samplesheets/PairedEnd.yml",
  "SmallRna.bowtieIndexFiles": [
    "/data/reference/bowtie/reference.1.ebwt",
    "/data/reference/bowtie/reference.2.ebwt",
    "/data/reference/bowtie/reference.3.ebwt",
    "/data/reference/bowtie/reference.4.ebwt",
    "/data/reference/bowtie/reference.rev.1.ebwt",
    "/data/reference/bowtie/reference.rev.2.ebwt"
  ],
  "SmallRna.gtfFiles": [
    {"path": "/data/ensembl.gtf",
      "idattr": "gene_id"},
    {"path": "/data/hsa.gff3",
      "featureType": "miRNA"}
  ],
  "SmallRna.sampleWorkflow.SampleWorkflow.QualityControl.adapterForward": "AGATCGGAAGAG",
  "SmallRna.sampleWorkflow.SampleWorkflow.QualityControl.adapterReverse": "GATCGTCGGACT",
  "SmallRna.dockerImagesFile": "dockerImages.yml"
}

And the associated samplesheet might look like this:

Saved as csv format it will look like this.

"sample","library","read","R1","R1_md5","R2","R2_md5"
"patient1","lib1","lane1","/home/user/data/patient1/R1.fq.gz","181a657e3f9c3cde2d3bb14ee7e894a3","/home/user/data/patient1/R2.fq.gz","ebe473b62926dcf6b38548851715820e"
"patient2","lib1","lane1","/home/user/data/patient2/lane1_R1.fq.gz","7e79b87d95573b06ff2c5e49508e9dbf","/home/user/data/patient2/lane1_R2.fq.gz","dc2776dc3a07c4f468455bae1a8ff872"
"patient2","lib1","lane2","/home/user/data/patient2/lane2_R1.fq.gz","18e9b2fef67f6c69396760c09af8e778","/home/user/data/patient2/lane2_R2.fq.gz","72209cc64510827bc3f849bab00dfe7d"

The pipeline also supports tab- and ;-delimited files.

Output

This workflow will output the trimmed fastq files, bam files and count files in a separate folder per sample. It will also output a merged counts file in the output directory.

Contact

For any question about running this pipeline and feature requests, please use the github issue tracker or contact the SASC team directly at: sasc@lumc.nl.