nf-core/callingcards

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Introduction

nf-core/callingcards is a bioinformatics pipeline that can be used to process raw Calling Cards data obtained from either mammals (human, mouse) or yeast. It takes a samplesheet, which describes the sample names, paths to the fastq files, and paths to the barcode details json files, a parameter which identifies the organism type (either mammals or yeast) and either the name of the reference genome on igenomes or paths to the fasta/gff. It then parses the reads, counts the number of calling cards insertions, and provides some QC metrics.

1. Prepare the Genome
   • optional masking with bedtools
   • Create aligner specific indicies. Available aligners:
     • bowtie
     • bowtie2
     • bwa
     • bwamem2
   • Create a genome index with samtools
2. Prepare the Reads
   • Read QC (FastQC)
   • Split reads
     • Yeast: demultiplex by barcode (callingCardsTools)
     • Mammals: split for parallel processing (seqkit)
       • Mammals: extract barcode to fastq read ID( UMItools)
   • Optionally trim reads (Trimmomatic)])
3. Align
   • Any one of the aligners listed above in ‘Prepare the Genome’
   • Alignment QC (samtools, picard,rseqc)))
4. Count Hops (callingCardsTools)
5. Present QC data (MultiQC)

Usage

First, prepare a samplesheet with your input data according to your organism.

A Yeast samplesheet will look like:

yeast_samplesheet.csv:

sample,fastq_1,fastq_2,barcode_details
run_6177,run_6177_sample_R1.fastq.gz,run_6177_sample_R2.fastq.gz,run_6177_barcode_details.json

Each row represents a multiplexed fastq file where the barcode_details.json file describes the barcodes which correspond to each transcription factor in the library.

A mammals (human, mouse) samplesheet will look like:

mammals_samplesheet.csv:

sample,fastq_1,fastq_2,barcode_details
midbrain_rep3,midbrain_rep3_R1.fastq.gz,,barcode_details.json

Note that currently, the mammals workflow expects only R1 reads.

Now, you can run the pipeline using:

nextflow run nf-core/callingcards \
   -profile <docker/singularity/.../institute>,<default_yeast/default_mammals> \
   --input samplesheet.csv \
   --genome <igenomes_name (only required for mammals)> \
   --outdir <OUTDIR>

Note that the default_yeast and default_mammals profiles are provided for convenience. You should check the parameters which these set to ensure that they are what you need for your data.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/callingcards is implemented in nextflow by Chase Mateusiak. It was adapted from scripts written by:

• Rob Mitra

We thank the following people for their extensive assistance in the development of this pipeline:

• Allen Yen
• Mari Gachechiladze
• Joseph Dougherty

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

For further information or help, don’t hesitate to get in touch on the Slack #callingcards channel (you can join with this invite).

Citations

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

You can cite the nf-core publication as follows:

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.