ArticleBioinformatics (Oxford, England)2015
Gaussian process test for high-throughput sequencing time series: application to experimental evolution.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Direct estimation of genotype fitness from time series.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.bioRxiv : the preprint server for biology · 2026Article
- Linkage-aware inference of fitness from short-read time-series genomic data.Virus evolution · 2026Article
- A supervised Bayesian method for time (re)annotation of transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Selection Estimation from Genetic Time-Series Data: Effects of Limited Sampling and Genetic Drift.Molecular biology and evolution · 2025Article
- Bait-ER: A Bayesian method to detect targets of selection in Evolve-and-Resequence experiments.Journal of evolutionary biology · 2023Article
- Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.The annals of applied statistics · 2022Article
- Inferring Epistasis from Genetic Time-series Data.Molecular biology and evolution · 2022Article
- Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies.Molecular biology and evolution · 2019Article
- Benchmarking software tools for detecting and quantifying selection in evolve and resequencing studies.Genome biology · 2019Article
- Seasonal Variation in Genome-Wide DNA Methylation Patterns and the Onset of Seasonal Timing of Reproduction in Great Tits.Genome biology and evolution · 2019Article
- Inferring population genetics parameters of evolving viruses using time-series data.Virus evolution · 2019Article
- GPrank: an R package for detecting dynamic elements from genome-wide time series.BMC bioinformatics · 2018Article
- MimicrEE2: Genome-wide forward simulations of Evolve and Resequencing studies.PLoS computational biology · 2018Article
- Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.Genetics · 2018Article
- Evaluating genetic drift in time-series evolutionary analysis.Journal of theoretical biology · 2018Article
- Identifying consistent allele frequency differences in studies of stratified populations.Methods in ecology and evolution · 2017Article
- Selection for long and short sleep duration in Drosophila melanogaster reveals the complex genetic network underlying natural variation in sleep.PLoS genetics · 2017Article
- Quantifying Selection with Pool-Seq Time Series Data.Molecular biology and evolution · 2017Article
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5 authors.
Funding
Abstract
motivationRecent 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.
resultsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.