Genomic responses to socio-sexual environment in male Drosophila melanogaster exposed to conspecific rivals

Socio-sexual environments have profound effects on fitness. Local sex ratios can alter the threat of sexual competition, to which males respond via plasticity in reproductive behaviours and ejaculate composition. In Drosophila melanogaster, males detect the presence of conspecific mating rivals prior to mating using multiple, redundant sensory cues. Males that respond to rivals gain significant fitness benefits by altering mating duration and ejaculate composition. Here we investigated the underlying genome-wide changes involved. We used RNA-seq to analyse male transcriptomic responses 2, 26 and 50h after exposure to rivals, a time period that was previously identified as encompassing the major facets of male responses to rivals. The results showed a strong early activation of multiple sensory genes in the head-thorax (HT), prior to the expression of any phenotypic differences. This gene expression response was reduced by 26h, at the time of maximum phenotypic change, and shut off by 50h. In the abdomen (A) fewer genes changed in expression and gene expression responses appeared to increase over time. The results also suggested that different sets of functionally equivalent genes might be activated in different replicates. This could represent a mechanism by which robustness is conferred upon highly plastic traits. Overall, our study reveals that mRNA-seq can identify subtle genomic signatures characteristic of flexible behavioural phenotypes

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs samplespecific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches