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Physlr: Next-Generation Physical Maps
by
Amirhossein Afshinfard, Shaun D. Jackman, Johnathan Wong, Lauren Coombe, Justin Chu, Vladimir Nikolic, Gokce Dilek, Yaman Malkoç, René L. Warren and Inanc Birol
Cited by 4 | Viewed by 3178
Abstract
While conventional physical maps helped build most of the reference genomes we use today, generating the maps was prohibitively expensive, and the technology was abandoned in favor of whole-genome shotgun sequencing (WGS). However, genome assemblies generated using WGS data are often less contiguous.
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While conventional physical maps helped build most of the reference genomes we use today, generating the maps was prohibitively expensive, and the technology was abandoned in favor of whole-genome shotgun sequencing (WGS). However, genome assemblies generated using WGS data are often less contiguous. We introduce Physlr, a tool that leverages long-range information provided by some WGS technologies to construct next-generation physical maps. These maps have many potential applications in genome assembly and analysis, including, but not limited to, scaffolding. In this study, using experimental linked-read datasets from two humans, we used Physlr to construct chromosome-scale physical maps (NGA50s of 52 Mbp and 70 Mbp). We also demonstrated how these physical maps can help scaffold human genome assemblies generated using various sequencing technologies and assembly tools. Across all experiments, Physlr substantially improved the contiguity of baseline assemblies over state-of-the-art linked-read scaffolders.
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