nf-core pipeline · wgs

nf-core/genomicrelatedness

Infers genetic relatedness from low-coverage whole-genome sequencing data.

Run it with the research agent Documentation Source code

What nf-core/genomicrelatedness is

nf-core/genomicrelatedness is a bioinformatics pipeline designed to estimate pairwise genetic relatedness from low-coverage whole-genome sequencing (lcWGS, also known as shallow WGS or sWGS) data. Originally written by Thomas Isensee in collaboration with Gisela H. Kopp and Till Dorendorf as part of the bwRSE4HPC initiative, it addresses the challenge of making accurate kinship inferences when sequencing data are sparse.

For many non-model organisms, reference variant sets for base quality score recalibration (BQSR) are missing. This workflow addresses this limitation by offering an automated, iterative variant bootstrapping procedure to generate high-confidence variant sets directly from the data. It then applies joint variant calling and maximum-likelihood methods based on genotype likelihoods to reliably infer relatedness.

The pipeline outputs quality control summaries, alignment files, filtered cohort VCFs, and pairwise relatedness matrices, providing an end-to-end processing path from raw sequencing reads or pre-aligned files.

What the pipeline does

The pipeline executes in four main sections followed by multi-tool reporting. Preprocessing begins with reference genome indexing using BWA-mem2, SAMtools, and GATK4, alongside interval generation using gawk and split_intervals. Raw reads (FASTQ or SPRING) are quality-filtered with fastp, aligned using BWA-mem2, sorted and merged using SAMtools, and deduplicated using GATK4 MarkDuplicates to output CRAM files. Sequencing metrics are evaluated via MultiQC using preseq, SAMtools stats, and mosdepth. If pre-aligned BAM or CRAM files are supplied, preprocessing can be omitted.

For base recalibration when no known truth set exists, an optional bootstrapping workflow iteratively runs joint calling (GATK4 HaplotypeCaller, GenomicsDBImport, GenotypeGVCFs) and hard filtering (GATK4 VariantFiltration) to generate a stable high-confidence reference set, followed by GATK4 ApplyBQSR. Parallel variant calling is subsequently conducted using both GATK4 and BCFtools (bcftools mpileup and call). The calls are intersected with bcftools isec and filtered with vcftools. Finally, genotype likelihoods are analysed using maximum-likelihood estimation via NgsRelatev2 to deduce identity-by-descent (IBD) and pairwise relatedness matrices.

Key facts

Pipeline engine: Nextflow
Input formats: FASTQ, SPRING, BAM, CRAM, FASTA, CSV samplesheet
Output formats: CRAM, CRAI, VCF, BED, HTML (MultiQC report), pairwise relatedness matrices
Primary alignment tool: BWA-mem2
Variant callers: GATK4, BCFtools
Relatedness estimator: NgsRelatev2
Primary developers: Thomas Isensee, Gisela H. Kopp, Till Dorendorf

What you can use it for

Relatedness Estimation from Low-Coverage Sequencing: Inferring pairwise kinship and identity-by-descent across samples with shallow or sparse whole-genome sequencing coverage using genotype likelihoods.
Non-Model Organism Variant Discovery: Generating de novo high-confidence variant sets via automated multi-round bootstrapping when curated reference variant databases are absent.
Cross-Caller Variant Intersection: Deriving robust cohort VCFs by executing GATK4 and BCFtools variant calling in parallel and retaining intersecting, thinned variant sites.

Run it here

The research agent runs nf-core/genomicrelatedness 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.

Ask the research agent

Strengths and limits

Good at

Employs genotype likelihood-based estimation via NgsRelatev2, avoiding the high error rates of naive genotype calling on shallow sequencing data.
Provides an automated bootstrapping workflow to generate stable reference variant sets for organisms lacking known variant resources.
Offers flexible entry points, allowing input of raw FASTQ or SPRING files, or pre-aligned BAM and CRAM files to bypass preprocessing.
Incorporates cross-tool variant intersection between GATK4 and BCFtools to improve variant call confidence.

Watch out for

If bootstrapping rounds are specified, the setting is restricted to an integer between 1 and 3.
Preprocessing is designed for paired-end sequencing reads.

Questions

Can I run nf-core/genomicrelatedness without my own cluster?

What licence is nf-core/genomicrelatedness under?

MIT, like every nf-core pipeline.

Can the pipeline be run on existing BAM or CRAM alignments?

Yes. The preprocessing section can be skipped entirely by providing existing BAM or CRAM files directly in the samplesheet.

How does the pipeline handle base quality score recalibration when no known variants exist?

It provides an optional variant bootstrapping mode that iteratively discovers, hard-filters, and refines high-confidence variants using GATK4 to produce a reference set for recalibration.

Which software tools perform the variant calling?

The pipeline runs both GATK4 (HaplotypeCaller, GenomicsDBImport, GenotypeGVCFs) and BCFtools (mpileup, call) in parallel by default, and can intersect their calls.

How is relatedness calculated?

Relatedness is inferred through maximum-likelihood estimation of identity-by-descent from genotype likelihoods using NgsRelatev2.

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/genomicrelatedness's own README. Statements citing a figure that does not appear there are removed before publication. Licence and running details come from our registry.