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

TL;DR

This paper introduces a beta-binomial Gaussian process model for analyzing high-throughput sequencing time series data, effectively identifying significant changes in allele frequencies over time, especially in experimental evolution studies.

Contribution

The novel BBGP model combines beta-binomial and Gaussian processes to account for sequencing uncertainty and temporal dynamics, improving detection of significant features in HTS data.

Findings

Outperforms classical methods in simulation tests

Effectively incorporates replicate and intermediate time point data

Successfully applied to Drosophila temperature adaptation data

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 to 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 analysing 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 (BBGP) 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: R software implementing the test is available at https://github.com/handetopa/BBGP