Reference flow: reducing reference bias using multiple population genomes
Abstract 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 inclu...
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Online Access: | https://doi.org/10.1186/s13059-020-02229-3 |
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doaj-6e4b79f80f3144f289b5c65dca87d3762021-01-10T12:58:51ZengBMCGenome Biology1474-760X2021-01-0122111710.1186/s13059-020-02229-3Reference flow: reducing reference bias using multiple population genomesNae-Chyun Chen0Brad Solomon1Taher Mun2Sheila Iyer3Ben Langmead4Department of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityAbstract 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.https://doi.org/10.1186/s13059-020-02229-3 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nae-Chyun Chen Brad Solomon Taher Mun Sheila Iyer Ben Langmead |
spellingShingle |
Nae-Chyun Chen Brad Solomon Taher Mun Sheila Iyer Ben Langmead Reference flow: reducing reference bias using multiple population genomes Genome Biology |
author_facet |
Nae-Chyun Chen Brad Solomon Taher Mun Sheila Iyer Ben Langmead |
author_sort |
Nae-Chyun Chen |
title |
Reference flow: reducing reference bias using multiple population genomes |
title_short |
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_sort |
reference flow: reducing reference bias using multiple population genomes |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2021-01-01 |
description |
Abstract 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. |
url |
https://doi.org/10.1186/s13059-020-02229-3 |
work_keys_str_mv |
AT naechyunchen referenceflowreducingreferencebiasusingmultiplepopulationgenomes AT bradsolomon referenceflowreducingreferencebiasusingmultiplepopulationgenomes AT tahermun referenceflowreducingreferencebiasusingmultiplepopulationgenomes AT sheilaiyer referenceflowreducingreferencebiasusingmultiplepopulationgenomes AT benlangmead referenceflowreducingreferencebiasusingmultiplepopulationgenomes |
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1724341981164863488 |