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

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

SUBMITTER: Topa H 

PROVIDER: S-EPMC4443671 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature