Biology subjects

Rigaill, G.

Publications and source records attributed to Rigaill, G..

2 recordsLinked to original sources

bioRxiv2023

A genome-wide segmentation approach for the detection of selection footprints

MotivationIn population genetics, the detection of genomic regions under positive selection is essential to understand the genetic basis of locally adaptive trait variation. We propose a principled approach to detect those regions that combines a robust moment based FST estimator with a segmentation algorithm. ResultsOur approach allows for pairwise comparisons of populations and does not require any prior knowledge about the size of the regions to be detected. The procedure runs within seconds even for large genome datasets with millions of SNPs, and provides a complete landscape of the FST distribution over the chromosome. The procedure comes with a grounded estimator of the baseline FST level, allowing the detection of regions exhibiting high departures from this reference value. The potential of our procedure is illustrated in two applications in animal and human population genetics. We were able to recover in a matter of seconds regions known to be under selection, often with greater precision than what was reported in previous studies. AvailabilityOur approach is implemented in the fst4pg R package available from the CRAN repository. The Sheep dataset is downloadable from the Zenodo repository https://doi.org/10.5281/zenodo.237116. The 1000 Genome dataset is downloadable from ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502

bioinformatics↗

bioRxiv2023

DiffSegR: An RNA-Seq data driven method for differential expression analysis using changepoint detection

To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3/5)-degradation of rRNA, mRNA, and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html.

bioinformatics↗