Introduction

birneylab/stitchimpute is a bioinformatics pipeline that uses STITCH for imputing genotypes from low-coverage NGS data in a population. It can also help in the selection of the ideal parameters for the imputation, and in the refinement of the SNP set used. It can compare the imputation results against some ground truth (i.e. high-coverage samples) for performance evaluation and parameter/SNP set refinement.

Disclaimer: this pipeline uses the nf-core template but it is not part of nf-core itself.

1. Downsample high-coverage cram files (samtools; optional)
2. Run joint imputation with STITCH on high and low coverage cram files (STITCH)
3. Compare imputation results to ground truth variants (glimpse2 concordance; optional)
4. Plot imputation performance stats (ggplot2)

Usage

Note If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,cram,crai
sample1,/path/to/sample1.cram,/path/to/sample1.cram.crai
sample2,/path/to/sample2.cram,/path/to/sample2.cram.crai

Each row represents a sample with its associated cram file and crai file.

Now, you can run the pipeline using:

nextflow run birneylab/stitchimpute \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

Warning: Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

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

Pipeline output

For more details about the output files and reports, please refer to the output documentation.

Credits

birneylab/stitchimpute was originally written by Saul Pierotti.

Citations

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

The main citation for birneylab/stitchimpute is:

Genotype imputation in F2 crosses of inbred lines

Saul Pierotti, Bettina Welz, Mireia Osuna-López, Tomas Fitzgerald, Joachim Wittbrodt, Ewan Birney

Bioinformatics Advances, Volume 4, Issue 1, 2024, doi: 10.1093/bioadv/vbae107

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.