strkit 0.3.0rc1

A toolkit for analyzing variation in short(ish) tandem repeats.

STRkit

A toolkit for analyzing variation in short(ish) tandem repeats.

Warning

Bootstrapping performance may be hindered on systems with OpenMP without additional configuration. See below for details on how to fix this.

Installation

STRkit can be installed from PyPI via pip with the following command:

python -m pip install strkit

Commands

strkit call: Genotype caller with bootstrapped confidence intervals

A Gaussian mixture model tandem repeat genotype caller for long read data. STRkit is tuned specifically for high-fidelity long reads, although other long read data should still work.

Features:

• Performant, vectorized (thanks to parasail) estimates of repeat counts from high-fidelity long reads and a supplied catalog of TR loci and motifs.
• Re-weighting of longer reads, to compensate for their lower likelihood of observation.
  • Whole-genome and targeted genotyping modes to adjust this re-weighting.
• Parallelized for faster computing on clusters and for ad-hoc fast analysis of single samples.
• 95% confidence intervals on calls via a user-configurable optional parametric bootstrapping process.

Usage:

strkit call \
  path/to/read/file.bam \  # [REQUIRED] At least one indexed read file (BAM/CRAM)
  --ref path/to/reference.fa.gz \  # [REQUIRED] Indexed FASTA-formatted reference genome
  --loci path/to/loci.bed \  # [REQUIRED] TRF-formatted (or 4-col, with motif as last column) list of loci to genotype
  --min-reads 4 \  # Minimum number of supporting reads needed to make a call
  --min-allele-reads 2 \  # Minimum number of supporting reads needed to call a specific allele size 
  --flank-size 70 \  # Size of the flanking region to use on either side of a region to properly anchor reads

If more than one read file is specified, the reads will be pooled. This can come in handy if you have e.g. multiple flow cells of the same sample split into different BAM files, or the reads are split by chromosome.

If you want to output a full call report, you can use the --json output-file.json argument to specify a path to output a more detailed JSON document to. This document contains 99% CIs, peak labels, and some other information that isn't included in the normal TSV file.

Note on OpenMP Threading

Slow performance can result from running strkit call or strkit re-call on a system with OpenMP, due to a misguided attempt at multithreading under the hood somewhere in Numpy/Scipy (which doesn't work here due to repeated initializations of the Gaussian mixture model.) To fix this, set the following environment variable before running:

export OMP_NUM_THREADS=1

All optional flags:

• --min-reads ##: Minimum number of supporting reads needed to make a call. Default: 4
• --min-allele-reads ##: Minimum number of supporting reads needed to call a specific allele size. Default: 2
• --flank-size ##: Size of the flanking region to use on either side of a region to properly anchor reads. Default: 70
• --targeted: Turn on targeted genotyping mode, which re-weights longer reads differently. Use this option if the alignment file contains targeted reads, e.g. from PacBio No-Amp Targeted Sequencing. Default: off
• --num-bootstrap ###: Now many bootstrap re-samplings to perform. Default: 100
• --sex-chr ??: Sex chromosome configuration. Without this, loci in sex chromosomes will not be genotyped. Can be any configuration of Xs and Ys; only count matters. Default: none
• --json [path]: Path to output JSON call data to. JSON call data is more detailed than the stdout TSV output. Default: none
• --no-tsv: Suppresses TSV output to stdout. Without --json, no output will be generated, which isn't very helpful. Default: TSV output on

strkit visualize: Call visualizer

STRkit bundles a call visualization tool which takes as input a BAM file and a JSON call file from using the --json flag with strkit call.

It starts a web server on your local machine; the visualizations can be interacted with in a web browser.

To use the tool, run the following command:

strkit visualize path/to/my-alignment.bam \ 
  --ref hg38 \  # or hg19
  --json path/to/my-calls.json \
  -i 1  # 1-indexed offset in JSON file for locus of interest. Default is 1 if left out.

This will output something like the following:

 * Serving Flask app 'strkit.viz.server' (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on
 * Running on http://localhost:5011 (Press CTRL+C to quit)
...

You can then go to the URL listed, http://localhost:5011, on your local machine to see the visualization tool:

STRkit browser histogram, showing an expansion in the HTT gene.

The same expansion, shown in the igv.js browser. Note the insertions on the left-hand side in most reads, and the heterozygous copy number pattern.

To exit the tool, press Ctrl-C in your command line window as mentioned in the start-up instructions.

strkit re-call: Genotype re-caller

This command has a similar feature-set as strkit call, but is designed to be used with the output of other long-read STR genotyping methods to refine the genotype estimates when calling from HiFi reads.\

Features:

• Support for re-calling output from tandem-genotypes, RepeatHMM, and Straglr
• Strand resampling / bias correction (for use with the tandem-genotypes program)
• 95% confidence intervals on calls via user-configurable bootstrapping

Notes:

• --min-allele-reads will affect the confidence intervals given by the bootstrap process, especially in low-coverage loci. This should be set depending on the read technology being used; something like a single PacBio HiFi read generally contains higher-quality information than a single PacBio CLR read, for example.

strkit mi: Mendelian inheritance analysis

This tool is currently in development and in a very unfinished state. However, the following features will be in the final release:

• Mendelian inheritance % (MI) calculations for many common TR genotyping tools for both long/short reads
• Confidence-interval MI calculations for the genotyping tools which report CIs
• Reports of loci (potentially of interest) which do not respect MI

Copyright and License

Copyright (C) 2021-2022 David Lougheed & McGill University

Portions (viz) copyright (C) 2021-2022 David Lougheed

Portions of viz/templates/browser.html copyright (C) 2021-2022 Observable, Inc. Used under the terms of the ISC license.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.