  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
