digenoma-lab / fastqc-nf

fastqc-nf

Quality control of raw sequencing reads

Description

Perform quality control of Fasta files.

Dependencies

1. This pipeline is based on nextflow. As we have several nextflow pipelines, we have centralized the common information in the IARC-nf repository. Please read it carefully as it contains essential information for the installation, basic usage and configuration of nextflow and our pipelines.
2. FastQC: see official installation here. You can avoid installing all the external software by only installing Docker (not available yet). See the IARC-nf repository for more information.)
3. Multiqc: see official installation here. You can avoid installing all the external software by only installing Docker (not available yet). See the IARC-nf repository for more information.)

BAM input files

In order to process BAM files, we convert fastq files to bam files with:

4. samtools

Input

Name	Description
--input_folder	Folder containing FASTQ files
--output_folder	Path to output folder

Parameters

Optional

Name	Example value	Description
--ext	fastq.gz	Extension of files
--multiqc_config	none	config yaml file for multiqc
--cpu	2	Number of cpu used by fastqc
--mem	10	Size of memory used for mapping (in GB)

Flags

Flags are special parameters without value.

Name	Description
--help	Display help

Usage

nextflow run digenoma-lab/fastqc-nf -r v1.1 -profile singularity --input_folder input --output_folder results

To run the pipeline with docker or conda instead of singularity, just replace "-profile singularity" with "-profile docker" or "-profile conda", respectively. To run with your own local installation of softwares, just remove "-profile singularity"

Output

Type	Description
multiqc_fastqc_report.html	multiQC report for fastQC
multiqc_fastqc_report_data	data used for the multiQC report HTMLs

Former developpers

Name	Email	Description
Nicolas Alcala*	[e-mail]	Developer to contact for support
Tiffany Delhomme		Developer