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.
Highlights
The Human Genome Project produced the first full draft of the human genome sequence (International Human Genome Sequencing Consortium, 2001)
Higher quality reference genome sequences improve the mapping and alignment of sequence read data, but present challenges for integrating data mapped to other genome assembly versions
The workflow of GenomeWarp is as follows (Supplementary Fig. S1): an input Genome VCF (gVCF) is modified into source variants and confidently called source regions
Summary
The Human Genome Project produced the first full draft of the human genome sequence (International Human Genome Sequencing Consortium, 2001). The assembly of the human genome has been refined and updated multiple times (International Human Genome Sequencing Consortium, 2004). Higher quality reference genome sequences improve the mapping and alignment of sequence read data, but present challenges for integrating data mapped to other genome assembly versions. Many tools perform genomic region lift over, including UCSC LiftOver (Kuhn et al, 2013) and CrossMap (Zhao et al, 2014). These tools support lift over of multiple data formats, with
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