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

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ABSTRACT Motivation Genomes 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 …at 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. Results We 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 Implementation GNU GPL at https://github.com/BenoitMorel/AleRax and data are made available at https://cme.h-its.org/exelixis/material/alerax_data.tar.gz . Contact Benoit.Morel@h-its.org Supplementary information Supplementary material is available.

Authors: Benoit Morel, Tom A. Williams, Alexandros Stamatakis, Gergely J. Szöllősi

Date Published: 7th Oct 2023

Publication Type: Journal

The role of flow in the self-assembly of dragline spider silk proteins

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Hydrodynamic flow in the spider duct induces conformational changes in dragline spider silk proteins (spidroins) and drives their assembly, but the underlying physical mechanisms are still elusive. … Here we address this challenging multiscale problem with a complementary strategy of atomistic and coarse-grained molecular dynamics simulations with uniform flow. The conformational changes at the molecular level were analyzed for single-tethered spider silk peptides. Uniform flow leads to coiled-to-stretch transitions and pushes alanine residues into β sheet and poly-proline II conformations. Coarse-grained simulations of the assembly process of multiple semi-flexible block copolymers using multi-particle collision dynamics reveal that the spidroins aggregate faster but into low-order assemblies when they are less extended. At medium-to-large peptide extensions (50%–80%), assembly slows down and becomes reversible with frequent association and dissociation events, whereas spidroin alignment increases and alanine repeats form ordered regions. Our work highlights the role of flow in guiding silk self-assembly into tough fibers by enhancing alignment and kinetic reversibility, a mechanism likely relevant also for other proteins whose function depends on hydrodynamic flow.

Authors: Ana M. Herrera-Rodríguez, Anil Kumar Dasanna, Csaba Daday, Eduardo R. Cruz-Chú, Camilo Aponte-Santamaría, Ulrich S. Schwarz, Frauke Gräter

Date Published: 5th Oct 2023

Publication Type: Journal

CO 2 on Graphene: Benchmarking Computational Approaches to Noncovalent Interactions

Abstract

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Authors: Christopher Ehlert, Anna Piras, Ganna Gryn’ova

Date Published: 3rd Oct 2023

Publication Type: Journal

Adaptive RAxML-NG: Accelerating Phylogenetic Inference under Maximum Likelihood using Dataset Difficulty

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Abstract Phylogenetic inferences under the maximum likelihood criterion deploy heuristic tree search strategies to explore the vast search space. Depending on the input dataset, searches from different …t, searches from different starting trees might all converge to a single tree topology. Often, though, distinct searches infer multiple topologies with large log-likelihood score differences or yield topologically highly distinct, yet almost equally likely, trees. Recently, Haag et al. introduced an approach to quantify, and implemented machine learning methods to predict, the dataset difficulty with respect to phylogenetic inference. Easy multiple sequence alignments (MSAs) exhibit a single likelihood peak on their likelihood surface, associated with a single tree topology to which most, if not all, independent searches rapidly converge. As difficulty increases, multiple locally optimal likelihood peaks emerge, yet from highly distinct topologies. To make use of this information, we introduce and implement an adaptive tree search heuristic in RAxML-NG, which modifies the thoroughness of the tree search strategy as a function of the predicted difficulty. Our adaptive strategy is based upon three observations. First, on easy datasets, searches converge rapidly and can hence be terminated at an earlier stage. Second, overanalyzing difficult datasets is hopeless, and thus it suffices to quickly infer only one of the numerous almost equally likely topologies to reduce overall execution time. Third, more extensive searches are justified and required on datasets with intermediate difficulty. While the likelihood surface exhibits multiple locally optimal peaks in this case, a small proportion of them is significantly better. Our experimental results for the adaptive heuristic on 9,515 empirical and 5,000 simulated datasets with varying difficulty exhibit substantial speedups, especially on easy and difficult datasets (53% of total MSAs), where we observe average speedups of more than 10×. Further, approximately 94% of the inferred trees using the adaptive strategy are statistically indistinguishable from the trees inferred under the standard strategy (RAxML-NG).

Authors: Anastasis Togkousidis, Oleksiy M Kozlov, Julia Haag, Dimitri Höhler, Alexandros Stamatakis

Date Published: 1st Oct 2023

Publication Type: Journal

Direction augmentation in the evaluation of armed conflict predictions

Abstract

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Authors: Johannes Bracher, Lotta Rüter, Fabian Krüger, Sebastian Lerch, Melanie Schienle

Date Published: 19th Sep 2023

Publication Type: Journal

Modeling graphs beyond hyperbolic: Graph neural networks in symmetric positive definite matrices

Abstract

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Authors: Wei Zhao, Federico López, J. Maxwell Riestenberg, Michael Strube, Diaaeldin Taha, Steve Trettel

Date Published: 18th Sep 2023

Publication Type: InProceedings

SN 2021gno: a Calcium-rich transient with double-peaked light curves

Abstract

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Authors: K. Ertini, G. Folatelli, L. Martinez, M. C. Bersten, J. P. Anderson, C. Ashall, E. Baron, S. Bose, P. J. Brown, C. Burns, J. M. DerKacy, L. Ferrari, L. Galbany, E. Hsiao, S. Kumar, J. Lu, P. Mazzali, N. Morrell, M. Orellana, P. J. Pessi, M. M. Phillips, A. L. Piro, A. Polin, M. Shahbandeh, B. J. Shappee, M. Stritzinger, N. B. Suntzeff, M. Tucker, N. Elias-Rosa, H. Kuncarayakti, C. P. Gutiérrez, A. Kozyreva, T. E. Müller-Bravo, T. -W. Chen, J. T. Hinkle, A. V. Payne, P. Székely, T. Szalai, B. Barna, R. Könyves-Tóth, D. Bánhidi, I. B. Bı́ró, I. Csányi, L. Kriskovits, A. Pál, Zs Szabó, R. Szakáts, K. Vida, J. Vinkó, M. Gromadzki, L. Harvey, M. Nicholl, E. Paraskeva, D. R. Young, B. Englert

Date Published: 8th Sep 2023

Publication Type: Journal

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