ComputationalRegulatoryGenomicsICL/customcage

Map

The first part of the pipeline is shown here:

The second part of the pipeline is shown here:

Pipeline overview

The pipeline is built using Nextflow and processes data using the following steps:

• Merge per-lane FASTQ files with the nf-core/cat_fastq module.
• Report raw read quality with FastQC.
• (optional) remove reads that DO NOT start with G.
• Trim adapters with TrimGalore.
• Report trimmed read quality with FastQC
• (optional; done by default) Trim the first G in forward reads with cutadapt.
• (optional) Build a STAR or bowtie2 index of the reference genome FASTA file, if the index is not provided. For the STAR index, use a mandatory genome annotation in a GTF format.
• Map trimmed reads onto the genome and filter alignments. If using STAR, then retain only the reads with at most 2 alignments (done within the STAR alignment module); if using bowtie2, then retain only the reads with $MAPQ\geq 20$ with samtools view.
• Convert wigs to bigWigs using UCSC wigtobigwig module.
• (optional) Remove PCR and optical duplicate reads with samtools markdup. See below for details.
• Sort the obtained BAM files using samtools sort.
• Index the sorted BAM files with samtools index.
• Assess mapping quality using samtools stats, samtools flagstat and samtools idxstats.
• MultiQC - Aggregate report describing results and QC from the mapping part of the pipeline
• Create a BSgenome package for the reference genome, if the package is not available.
• Create a CAGEexp object and call TSSs with CAGEr using a BSgenome package for the respective genome. If reads were mapped with STAR, bigWig files to use as input for CAGEr; if reads were mapped with bowtie2, then use MAPQ-filtered and sorted BAM files as CAGEr input.
• Analysis of CAGE reads according to the manual of CAGEr. Final output is a markdown document summarizing the results and QC, as well as tracks: bed and bigwig files, a set of intermediate RDS files, stand-alone plots (all shown or referenced in the report), and data tables.
• Pipeline information - Report metrics generated during the workflow execution

Usage and Outputs

Quickstart

sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz

Each row represents a fastq file (single-end) or a pair of fastq files (paired end).

Now, you can run the pipeline using:

nextflow run ComputationalRegulatoryGenomicsICL/customcage \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --gtf example.gtf \
   --outdir <OUTDIR>

For extended documentation about the input parameters and usage, please visit the usage documentation. About outputs you can read at the outputs page.

Credits

nf-core/customcageq has been developed by Sviatoslav Sidorov (@sidorov-si), Katalin Ferenc (@ferenckata), Damir Baranasic (@da-bar), Elena Gómez-Marín (@ElenaGoMa), and Pavel Nikitin (@nikitin-p).

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.