The first part of the pipeline is shown here:

The second part of the pipeline is shown here:

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

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.

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).

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.