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Genome graphs and the evolution of genome inference

The human reference genome is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to...

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Detalles Bibliográficos
Autores principales: Paten, Benedict, Novak, Adam M., Eizenga, Jordan M., Garrison, Erik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411762/
https://www.ncbi.nlm.nih.gov/pubmed/28360232
http://dx.doi.org/10.1101/gr.214155.116
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author Paten, Benedict
Novak, Adam M.
Eizenga, Jordan M.
Garrison, Erik
author_facet Paten, Benedict
Novak, Adam M.
Eizenga, Jordan M.
Garrison, Erik
author_sort Paten, Benedict
collection PubMed
description The human reference genome is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph-based models. Here, we survey various projects underway to build and apply these graph-based structures—which we collectively refer to as genome graphs—and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce.
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spelling pubmed-54117622017-05-16 Genome graphs and the evolution of genome inference Paten, Benedict Novak, Adam M. Eizenga, Jordan M. Garrison, Erik Genome Res Review The human reference genome is part of the foundation of modern human biology and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph-based models. Here, we survey various projects underway to build and apply these graph-based structures—which we collectively refer to as genome graphs—and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce. Cold Spring Harbor Laboratory Press 2017-05 /pmc/articles/PMC5411762/ /pubmed/28360232 http://dx.doi.org/10.1101/gr.214155.116 Text en © 2017 Paten et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
spellingShingle Review
Paten, Benedict
Novak, Adam M.
Eizenga, Jordan M.
Garrison, Erik
Genome graphs and the evolution of genome inference
title Genome graphs and the evolution of genome inference
title_full Genome graphs and the evolution of genome inference
title_fullStr Genome graphs and the evolution of genome inference
title_full_unstemmed Genome graphs and the evolution of genome inference
title_short Genome graphs and the evolution of genome inference
title_sort genome graphs and the evolution of genome inference
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411762/
https://www.ncbi.nlm.nih.gov/pubmed/28360232
http://dx.doi.org/10.1101/gr.214155.116
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