nf-core pipeline ·
nf-core/callingcards
Processes calling cards sequencing data from yeast and mammalian models to identify and quantify genomic insertions.
What nf-core/callingcards is
nf-core/callingcards is a bioinformatics pipeline designed to process raw Calling Cards sequencing data. The Calling Cards method records transcription factor binding events across the genome using transposon insertions, commonly referred to as hops. The pipeline was implemented in Nextflow by Chase Mateusiak, adapting original scripts by Rob Mitra, with development support from Allen Yen, Mari Gachechiladze, and Joseph Dougherty.
The software addresses data processing needs for both yeast and mammalian (human and mouse) experiments. Given a samplesheet detailing FASTQ files and barcode metadata, the pipeline handles reference indexing, read quality control, barcode-based demultiplexing or barcode extraction, alignment, and final counting of genomic insertions.
The project is currently archived and is no longer under active maintenance, meaning no new features or bug fixes are being published by the original team.
What the pipeline does
Processing starts with reference genome preparation, which includes optional masking using bedtools, index generation with samtools, and building aligner-specific indices for Bowtie, Bowtie 2, BWA, or BWAmem2. Raw read quality is initially evaluated using FastQC. Organism-specific read processing paths then diverge: yeast reads are demultiplexed by barcode using callingCardsTools, whereas mammalian reads are split for parallel processing with seqkit and their barcodes are extracted into FASTQ read identifiers via UMI-tools. Reads can also be trimmed using Trimmomatic.
Following read preparation, sequences are mapped to the genome using the selected aligner (Bowtie, Bowtie 2, BWA, or BWAmem2). Quality assessments for the resulting alignments are calculated using samtools, Picard, and RSeQC. Genomic insertion events (hops) are then quantified using callingCardsTools, and overall workflow quality metrics are compiled into a report by MultiQC.
Key facts
- Supported organisms
- Yeast, human, and mouse
- Pipeline engine
- Nextflow
- Latest release
- 1.0.0 (2024-05-24)
- Supported aligners
- Bowtie, Bowtie 2, BWA, BWAmem2
- Input formats
- FASTQ (paired-end for yeast, single-end for mammals), CSV samplesheet, JSON barcode details
- Maintenance status
- Archived
What you can use it for
- Yeast Calling Cards analysis
- Demultiplex multiplexed paired-end yeast reads using transcription factor barcode definitions and quantify insertion sites across the genome.
- Mammalian transcription factor mapping
- Process single-end sequencing libraries from mouse or human experiments by extracting barcodes into read headers prior to alignment and insertion counting.
- Multi-aligner mapping comparison
- Evaluate Calling Cards read alignment performance across different mapping algorithms, selecting between Bowtie, Bowtie 2, BWA, or BWAmem2.
Run it here
The research agent runs nf-core/callingcards with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
Strengths and limits
Good at
- Includes dedicated processing branches tailored to the specific library architectures of yeast and mammalian protocols.
- Offers flexible alignment options supporting Bowtie, Bowtie 2, BWA, and BWAmem2.
- Provides preconfigured execution profiles for yeast and mammalian workflows.
- Integrates comprehensive quality reporting across read, alignment, and insertion stages via MultiQC.
Watch out for
- The pipeline is archived and no longer actively maintained or patched.
- Mammalian workflows only support single-end (R1) sequencing reads.
- Requires a specifically formatted barcode details JSON file alongside the samplesheet.
Questions
Can I run nf-core/callingcards without my own cluster?
The research agent runs nf-core/callingcards with Nextflow on its own machine — up to 32 cores, 256 GiB of memory and 24 hours per run, which covers the test profile and real single-sample runs. It builds the samplesheet, checks it against the pipeline's schema and reads the results back. When a run is too big for one machine, it gives you the exact command for your own cluster instead.
What licence is nf-core/callingcards under?
MIT, like every nf-core pipeline.
Which organisms are supported by the pipeline?
The pipeline is designed to handle Calling Cards sequencing data from yeast as well as mammals, specifically human and mouse.
Can paired-end sequencing data be analysed for mammalian samples?
No. The mammalian workflow expects only single-end R1 reads.
Which aligners can be used for mapping reads?
Users can specify any one of Bowtie, Bowtie 2, BWA, or BWAmem2.
What inputs are required to run the workflow?
The workflow requires a samplesheet CSV listing samples, paths to FASTQ files, and paths to barcode details JSON files, together with either an iGenomes reference name or direct paths to FASTA and GFF files.
Is the pipeline currently supported?
No, the repository is archived and is not receiving active maintenance, updates, or bug fixes.
How to cite it
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.
Written from nf-core/callingcards's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.