Biology subjects

Train, C.

Publications and source records attributed to Train, C..

3 recordsLinked to original sources

bioRxiv2024

EdgeHOG: fine-grained ancestral gene order inference at tree-of-life scale

Ancestral genomes are essential for studying the diversification of life from the last universal common ancestor to modern organisms. Methods have been proposed to infer ancestral gene order, but they lack scalability, limiting the depth to which gene neighborhood evolution can be traced back. We introduce edgeHOG, a tool designed for accurate ancestral gene order inference with linear time complexity. Validated on various benchmarks, edgeHOG was applied to the entire OMA orthology database, encompassing 2,845 extant genomes across all domains of life. This represents the first tree-of-life scale inference, resulting in 1,133 ancestral genomes. In particular, we reconstructed ancestral contigs for the last common ancestor of eukaryotes, dating back around 1.8 billion years, and observed significant functional association among neighboring genes. The method also dates gene adjacencies, revealing conserved histone clusters and rapid sex chromosome rearrangements, enabling computational inference of these features.

evolutionary biology↗

bioRxiv2023

Matreex: compact and interactive visualisation of large gene families provides evidence for loss of intraflagellar transport in a myxozoan

Studying gene family evolution strongly benefits from insightful visualisations. However, the evergrowing number of sequenced genomes is leading to increasingly larger gene families, which challenges existing gene tree visualisations. Indeed, most of them present users with a dilemma: display complete but intractable gene trees, or collapse subtrees, thereby hiding their childrens information. Here, we introduce Matreex, a new dynamic tool to scale-up the visualisation of gene families. Matreexs key idea is to use "phylogenetic" profiles, which are dense representations of gene repertoires, to minimise the information loss when collapsing subtrees. We illustrate Matreex usefulness with three biological applications. First, we demonstrate on the MutS family the power of combining gene trees and phylogenetic profiles to delve into precise evolutionary analyses of large multi-copy gene families. Secondly, by displaying 22 intraflagellar transport gene families across 622 species cumulating 5500 representatives, we show how Matreex can be used to automate large-scale analyses of gene presence-absence. Notably, we report for the first time the complete loss of intraflagellar transport in the myxozoan Thelohanellus kitauei. Finally, using the textbook example of visual opsins, we show Matreexs potential to create easily interpretable figures for teaching and outreach. Matreex is available from the Python Package Index (pip install matreex) with the source code and documentation available at https://github.com/DessimozLab/matreex.

evolutionary biology↗

bioRxiv2022

Multifaceted quality assessment of gene repertoire annotation with OMArk

Assessing the quality of protein-coding gene repertoires is critical in an era of increasingly abundant genome sequences for a diversity of species. State-of-the-art genome annotation assessment tools measure the completeness of a gene repertoire, but are blind to other types of errors, such as gene over-prediction or contamination. We developed OMArk, a software relying on fast, alignment-free sequence comparisons between a query proteome and precomputed gene families across the tree of life. OMArk assesses not only the completeness, but also the consistency of the gene repertoire as a whole relative to closely related species. It also reports likely contamination events. We validated OMArk with simulated data, then performed an analysis of the 1805 UniProt Eukaryotic Reference Proteomes, illustrating its usefulness for comparing and prioritizing proteomes based on their quality measures. In particular, we found strong evidence of contamination in 59 proteomes, and identified error propagation in avian gene annotation resulting from the use of a fragmented zebra finch proteome as reference. OMArk is available on GitHub (https://github.com/DessimozLab/OMArk), as a Python package on PyPi, and as an interactive online tool at https://omark.omabrowser.org/.

bioinformatics↗