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dc.contributor.authorFlagel, Lex E.
dc.contributor.authorBlackman, Benjamin K.
dc.contributor.authorFishman, Lila
dc.contributor.authorMonnahan, Patrick J.
dc.contributor.authorSweigart, Andrea
dc.contributor.authorKelly, John K.
dc.identifier.citationFlagel LE, Blackman BK, Fishman L, Monnahan PJ, Sweigart A, Kelly JK (2019) GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity. PLoS Comput Biol 15(4): e1006949.
dc.descriptionThis work is licensed under a Creative Commons Attribution 4.0 International License.en_US
dc.description.abstractUnderstanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.en_US
dc.publisherPublic Library of Scienceen_US
dc.rights© 2019 Flagel et al.en_US
dc.titleGOOGA: A platform to synthesize mapping experiments and identify genomic structural diversityen_US
kusw.kuauthorMonnahan, Patrick J.
kusw.kuauthorKelly, John K.
kusw.kudepartmentEcology & Evolutionary Biologyen_US
kusw.oanotesPer Sherpa Romeo 01/29/2021:

PLoS Computational Biology [Open panel below]Publication Information TitlePLoS Computational Biology [English] ISSNs Print: 1553-734X Electronic: 1553-7358 URL Publishers Public Library of Science [Commercial Publisher] International Society for Computational Biology (ISCB) [Associate Organisation] DOAJ Listing Requires APCYes [Data provided by DOAJ] [Close panel below]Publisher Policy Open Access pathways permitted by this journal's policy are listed below by article version. Click on a pathway for a more detailed view.

Published Version NoneCC BYPMC Any Website, Journal Website, +1 OA PublishingThis pathway includes Open Access publishing EmbargoNo Embargo LicenceCC BY 4.0 Copyright OwnerAuthors Publisher DepositPubMed Central Location Any Website Named Repository (PubMed Central) Journal Website ConditionsPublished source must be acknowledged with citation
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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© 2019 Flagel et al.
Except where otherwise noted, this item's license is described as: © 2019 Flagel et al.