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Reference flow: reducing reference bias using multiple population genomes
Most sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include geneti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780692/ https://www.ncbi.nlm.nih.gov/pubmed/33397413 http://dx.doi.org/10.1186/s13059-020-02229-3 |
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author | Chen, Nae-Chyun Solomon, Brad Mun, Taher Iyer, Sheila Langmead, Ben |
author_facet | Chen, Nae-Chyun Solomon, Brad Mun, Taher Iyer, Sheila Langmead, Ben |
author_sort | Chen, Nae-Chyun |
collection | PubMed |
description | Most sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02229-3). |
format | Online Article Text |
id | pubmed-7780692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77806922021-01-05 Reference flow: reducing reference bias using multiple population genomes Chen, Nae-Chyun Solomon, Brad Mun, Taher Iyer, Sheila Langmead, Ben Genome Biol Software Most sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13059-020-02229-3). BioMed Central 2021-01-04 /pmc/articles/PMC7780692/ /pubmed/33397413 http://dx.doi.org/10.1186/s13059-020-02229-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Chen, Nae-Chyun Solomon, Brad Mun, Taher Iyer, Sheila Langmead, Ben Reference flow: reducing reference bias using multiple population genomes |
title | Reference flow: reducing reference bias using multiple population genomes |
title_full | Reference flow: reducing reference bias using multiple population genomes |
title_fullStr | Reference flow: reducing reference bias using multiple population genomes |
title_full_unstemmed | Reference flow: reducing reference bias using multiple population genomes |
title_short | Reference flow: reducing reference bias using multiple population genomes |
title_sort | reference flow: reducing reference bias using multiple population genomes |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7780692/ https://www.ncbi.nlm.nih.gov/pubmed/33397413 http://dx.doi.org/10.1186/s13059-020-02229-3 |
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