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Gaussian process test for high-throughput sequencing time series : application to experimental evolution

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Date
01/06/2015
Author
Topa, Hande
Jónás, Ágnes
Kofler, Robert
Kosiol, Carolin
Honkela, Antti
Keywords
Alleles
Animals
Drosophila
Gene frequency
Genomics
High-throughput nucleotide sequencing
QH301 Biology
QH426 Genetics
DAS
BDC
R2C
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Abstract
MOTIVATION: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but also monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analyzing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth. RESULTS: We present the beta-binomial Gaussian process model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine it with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present simulations exploring different experimental design choices and results on real data from Drosophila experimental evolution experiment in temperature adaptation. AVAILABILITY AND IMPLEMENTATION: R software implementing the test is available at https://github.com/handetopa/BBGP.
Citation
Topa , H , Jónás , Á , Kofler , R , Kosiol , C & Honkela , A 2015 , ' Gaussian process test for high-throughput sequencing time series : application to experimental evolution ' , Bioinformatics , vol. 31 , no. 11 , pp. 1762-1770 . https://doi.org/10.1093/bioinformatics/btv014
Publication
Bioinformatics
Status
Peer reviewed
DOI
https://doi.org/10.1093/bioinformatics/btv014
ISSN
1367-4803
Type
Journal article
Rights
Copyright © The Author 2015. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Description
The work was supported under the European ERASysBio+ initiative project ‘SYNERGY’ through the Academy of Finland [135311]. A.H. was also supported by the Academy of Finland [259440] and H.T. was supported by Alfred Kordelin Foundation. R.K. was supported by ERC (ArchAdapt). A.J. is member of the Vienna Graduate School of Population Genetics which is supported by a grant of the Austrian Science Fund (FWF) [W1225-B20].
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/10258

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