Estimating IBD tracts from low coverage NGS data
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Estimating IBD tracts from low coverage NGS data. / Garrett Vieira, Filipe Jorge; Albrechtsen, Anders; Nielsen, Rasmus.
I: Bioinformatics, Bind 32, Nr. 14, 2016, s. 2096-2102.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Estimating IBD tracts from low coverage NGS data
AU - Garrett Vieira, Filipe Jorge
AU - Albrechtsen, Anders
AU - Nielsen, Rasmus
N1 - © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
PY - 2016
Y1 - 2016
N2 - MOTIVATION: The amount of IBD in an individual depends on the relatedness of the individual's parents. However, it can also provide information regarding mating system, past history and effective size of the population from which the individual has been sampled.RESULTS: Here, we present a new method for estimating inbreeding IBD tracts from low coverage NGS data. Contrary to other methods that use genotype data, the one presented here uses genotype likelihoods to take the uncertainty of the data into account. We benchmark it under a wide range of biologically relevant conditions and show that the new method provides a marked increase in accuracy even at low coverage.AVAILABILITY AND IMPLEMENTATION: The methods presented in this work were implemented in C/C ++ and are freely available for non-commercial use from https://github.com/fgvieira/ngsF-HMM CONTACT: fgvieira@snm.ku.dkSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - MOTIVATION: The amount of IBD in an individual depends on the relatedness of the individual's parents. However, it can also provide information regarding mating system, past history and effective size of the population from which the individual has been sampled.RESULTS: Here, we present a new method for estimating inbreeding IBD tracts from low coverage NGS data. Contrary to other methods that use genotype data, the one presented here uses genotype likelihoods to take the uncertainty of the data into account. We benchmark it under a wide range of biologically relevant conditions and show that the new method provides a marked increase in accuracy even at low coverage.AVAILABILITY AND IMPLEMENTATION: The methods presented in this work were implemented in C/C ++ and are freely available for non-commercial use from https://github.com/fgvieira/ngsF-HMM CONTACT: fgvieira@snm.ku.dkSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
KW - Journal Article
U2 - 10.1093/bioinformatics/btw212
DO - 10.1093/bioinformatics/btw212
M3 - Journal article
C2 - 27153648
VL - 32
SP - 2096
EP - 2102
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 14
ER -
ID: 167429256