Biology subjects

Szollosi, G.

Publications and source records attributed to Szollosi, G..

4 recordsLinked to original sources

bioRxiv2026

Models trained with noisy genomes extend bacterial phenotype prediction into deep time

Predicting phenotype from genotype in extant organisms is increasingly tractable through the accumulation of genome sequences and the development of machine-learning algorithms. Here we show that machine learning can be applied to reconstructed ancestral gene content, extending these predictions into the past. We trained models on a diverse set of bacterial phenotypes and found that introducing noise into gene content profiles allows predictions to generalize over larger evolutionary distances. For phenotypes with signal spread across many genes - such as metabolic oxygen use, cell envelope architecture and optimal growth temperature - noise augmentation extends resolution back to the root of the bacterial domain, while for other phenotypes - including GC content and sporulation - the range remains more limited. We therefore conclude that the last bacterial common ancestor (LBCA) was likely an anaerobic, double-membraned, and moderately thermophilic bacterium (46-75{degrees}C). Moreover, this work provides a general approach for learning about the genomic basis of phenotypes and drawing inferences about their early evolution.

evolutionary biology↗

bioRxiv2023

AleRax: A tool for species and gene tree co-estimation and reconciliation under a probabilistic model of duplication, transfer, and loss

MotivationGenomes are a rich source of information on the pattern and process of evolution across biological scales. How best to make use of that information is an active area of research in phylogenetics. Ideally, phylogenetic methods should not only model substitutions along gene trees, which explain differences between homologous gene sequences, but also the processes that generate the gene trees themselves along a shared species tree. To conduct accurate inferences, one needs to account for uncertainty at both levels, that is, in gene trees estimated from inherently short sequences and in their diverse evolutionary histories along a shared species tree. ResultsWe present AleRax, a software that can infer reconciled gene trees together with a shared species tree using a simple, yet powerful, probabilistic model of gene duplication, transfer, and loss. A key feature of AleRax is its ability to account for uncertainty in the gene tree and its reconciliation by using an efficient approximation to calculate the joint phylogenetic-reconciliation likelihood and sample reconciled gene trees accordingly. Simulations and analyses of empirical data show that AleRax is one order of magnitude faster than competing gene tree inference tools while attaining the same accuracy. It is consistently more robust than species tree inference methods such as SpeciesRax and ASTRAL-Pro 2 under gene tree uncertainty. Finally, AleRax can process multiple gene families in parallel thereby allowing users to compare competing phylogenetic hypotheses and estimate model parameters, such as DTL probabilities for genome-scale datasets with hundreds of taxa Availability and ImplementationGNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz. ContactBenoit.Morel@h-its.org Supplementary informationSupplementary material is available.

bioinformatics↗

bioRxiv2020

Pervasive selection biases inferences of the species tree

Despite the importance of natural selection in species evolutionary history, phylogenetic methods that take into account population-level processes typically ignore selection. The assumption of neutrality is often based on the idea that selection occurs at a minority of loci in the genome and is unlikely to compromise phylogenetic inferences significantly. However, genome-wide processes like GC-bias and some variation segregating at the coding regions are known to evolve in the nearly neutral range. As we are now using genome-wide data to estimate species trees, it is natural to ask whether weak but pervasive selection is likely to blur species tree inferences. We developed a polymorphism-aware phylogenetic model tailored for measuring signatures of nucleotide usage biases to test the impact of selection in the species tree. Our analyses indicate that while the inferred relationships among species are not significantly compromised, the genetic distances are systematically underestimated in a node-height dependent manner: i.e., the deeper nodes tend to be more underestimated than the shallow ones. Such biases have implications for molecular dating. We dated the evolutionary history of 30 worldwide fruit fly populations, and we found signatures of GC-bias considerably affecting the estimated divergence times (up to 23%) in the neutral model. Our findings call for the need to account for selection when quantifying divergence or dating species evolution. Significance statementAlthough little is known about the impact of natural selection on species tree estimation, expectations are that it occurs at a minority of loci in eukaryotic genomes and is thus unlikely to affect the divergence process. However, growing evidence suggests that a large amount of the genomic variation evolves under weak but pervasive selection (e.g., fixation biases created by GC-bias gene conversion). We tested the impact of unaccounted-for nearly neutral selection on species tree estimation and found that the estimated branch lengths are systematically biased. Our results highlight the need for selection-aware models in species tree estimation and molecular dating.

evolutionary biology↗

bioRxiv2017

MaxTiC: Fast Ranking Of A Phylogenetic Tree By Maximum Time Consistency With Lateral Gene Transfers

Lateral gene transfers between ancient species contain information about the relative timing of species diversification. Specifically, the ancestors of a donor species must have existed before the descendants of the recipient species. Hence, the detection of a transfer event can be translated into a time constraint between nodes of a phylogeny if the donor and recipient can be identified. When a set of transfers is detected by interpreting the phylogenetic discordance between gene trees and a species tree, the set of all deduced time constraints can be used to rank the species tree, i.e. order totally its internal nodes. Unfortunately lateral gene transfer detection is challenging and current methods produce a significant proportion of false positives. As a result, often, no ranking of the species tree is compatible with the full set of time constraints deduced from predicted transfers. Here we propose a method, implemented in a software called MaxTiC (Maximum Time Consistency), which takes as input a species tree and a series of (possibly inconsistent) time constraints between its internal nodes, weighted by confidence scores. MaxTiC outputs a ranked species tree compatible with a subset of constraints with maximum cumulated confidence score. We extensively test the method on simulated datasets, under a wide range of conditions that we compare to measures on biological datasets. In most conditions the obtained ranked tree is very close to the real one, confirming the potential of dating the history of life with transfers by maximizing time consistency. MaxTiC is freely available, distributed along with a documentation and several examples: https://github.com/ssolo/ALE/tree/master/maxtic.

evolutionary biology↗