GenomeWarp: an alignment-based variant coordinate transformation

Abstract

SummaryReference genomes are refined to reflect error corrections and other improvements. While this process improves novel data generation and analysis, incorporating data analyzed on an older reference genome assembly requires transforming the coordinates and representations of the data to the new assembly. Multiple tools exist to perform this transformation for coordinate-only data types, but none supports accurate transformation of genome-wide short variation. Here we present GenomeWarp, a tool for efficiently transforming variants between genome assemblies. GenomeWarp transforms regions and short variants in a conservative manner to minimize false positive and negative variants in the target genome, and converts over 99% of regions and short variants from a representative human genome.Availability and implementationGenomeWarp is written in Java. All source code and the user manual are freely available at https://github.com/verilylifesciences/genomewarp.Supplementary information Supplementary data are available at Bioinformatics online.

BCFtools/liftover: an accurate and comprehensive tool to convert genetic variants across genome assemblies

“…To measure which output records were not allelic primitive variants, we used BCFtools/view with option . We notice that these analyses do not attempt to recover non-polymorphic variants represented by different alleles across the two assemblies and which would have been missing from the input callset if all samples in the cohort had homozygous reference genotypes for that variant, something that GenomeWarp was designed to handle instead ( McLean et al 2019 ).…”

“…As Picard/LiftoverVcf can handle SNV records for which the two genome assemblies are represented by different alleles, SNVs are dropped mostly because of genomic loci missing from one genome assembly ( Ormond et al 2021 ). However, both Picard/LiftoverVcf and CrossMap/VCF cannot handle swapping the reference and alternate alleles for indel records leading to many of these variants either being dropped or being converted incorrectly with the risk of introducing biases in downstream analyses ( McLean et al 2019 , Lan et al 2022 , Weisburd et al 2023 ). There are three VCF liftover tools that can handle reference allele differences between assemblies for indel records: (i) Transanno/liftvcf ( http://github.com/informationsea/transanno ), which can also deal with multi-allelic VCF records; (ii) Genozip/DVCF ( Lan et al 2022 ), included in the Genozip software compression suite ( Lan et al 2020 ; Lan 2022 ); and (iii) GenomeWarp ( McLean et al 2019 ), which was designed to have higher accuracy at the cost of a larger number of variants being dropped.…”

“…However, both Picard/LiftoverVcf and CrossMap/VCF cannot handle swapping the reference and alternate alleles for indel records leading to many of these variants either being dropped or being converted incorrectly with the risk of introducing biases in downstream analyses ( McLean et al 2019 , Lan et al 2022 , Weisburd et al 2023 ). There are three VCF liftover tools that can handle reference allele differences between assemblies for indel records: (i) Transanno/liftvcf ( http://github.com/informationsea/transanno ), which can also deal with multi-allelic VCF records; (ii) Genozip/DVCF ( Lan et al 2022 ), included in the Genozip software compression suite ( Lan et al 2020 ; Lan 2022 ); and (iii) GenomeWarp ( McLean et al 2019 ), which was designed to have higher accuracy at the cost of a larger number of variants being dropped. Nevertheless, as allelic differences for indels between genome assemblies are not always handled correctly by any of the available liftover tools, indel records are always dropped or incorrectly converted at a higher rate than SNV records.…”

Genomic and Transcriptomic Analyses Reveals ZNF124 as a Critical Regulator in Highly Aggressive Medulloblastomas

“…Somatic variant analyses was conducted using the Genome Analysis Tool Kit (GATK) with best practices guidelines for genetic data preprocessing and variant calling ( Mckenna et al, 2010 ; Mclean et al, 2019 ). Briefly, the fastq data files from WES were mapped to human genome (hg38) by the Burrows-Wheeler Aligner (BWA) ( Li and Durbin, 2009 ) in the GATK4 module ( Mckenna et al, 2010 ).…”

Detecting archaic introgression and modeling multiple-wave admixture with ArchaicSeeker 2.0

“…Use the software liftOver ( Kuhn et al., 2013 ), GenomeWrap ( McLean et al., 2019 ), or other tools to do the conversion. ArchaicSeeker 2.0 requires that all the input data are in the same version (all GRCh37 or all GRCh38).…”