Biology subjects

Schmidt, H. A.

Publications and source records attributed to Schmidt, H. A..

4 recordsLinked to original sources

bioRxiv2026

Phylo-Movies: Animating Phylogenetic Trees from Sliding-Window Analyses

Sliding-window phylogenetic analyses of multiple sequence alignments (MSAs) generate sequences of phylogenetic trees that can reveal recombination and other sources of phylogenetic conflict, yet comparing trees across genomic windows remains challenging. Phylo-Movies is a browser-based tool, also available as a standalone desktop application, that decomposes topological differences between consecutive phylogenetic trees into interpretable subtree migrations and animates these transformations. We demonstrate its utility in two contexts: identifying recombintion breakpoints in norovirus genomes, where lineages shift from polymerase-based to capsid-based clustering at the ORF1/ORF2 junction, and detecting rogue taxa that change position across bootstrap replicates. Phylo-Movies complements summary statistics such as Robinson-Foulds distances by showing which lineages move, where they move from, and which new groupings they form. Phylo-Movies is freely available at https://github.com/enesberksakalli/phylo-movies, with a norovirus demonstration video at https://vimeo.com/1162400544, the first rogue taxon example at https://vimeo.com/1162561152, and the second example at https://vimeo.com/1162563101.

evolutionary biology↗

bioRxiv2023

Long non-coding RNAs contribute to DNA damage resistance in Arabidopsis thaliana

Efficient repair of DNA lesions is essential for faithful transmission of genetic information between somatic cells and for genome integrity across generations. Plants have multiple, partially redundant and overlapping DNA repair pathways, probably due to the less constricted germline and the inevitable exposure to light including higher energy wavelengths. Many proteins involved in DNA repair and their mode of actions are well described. In contrast, a role for DNA damage-associated RNA components, evident from many other organisms, is less well understood. Here, we have challenged young Arabidopsis thaliana plants with two different types of genotoxic stress and performed de novo assembly and transcriptome analysis. We identified three long non-coding RNAs (lncRNAs) that are lowly or not expressed under regular conditions but up-regulated or induced by DNA damage. To understand their potential role in DNA repair, we generated CRISPR/Cas deletion mutants and found that the absence of the lncRNAs impairs the recovery capacity of the plants from genotoxic stress. The genetic loci are highly conserved among world-wide distributed Arabidopsis accessions and within related species in the Brassicaceae group. Together, these results suggest that the lncRNAs have a conserved function in connection with DNA damage and provide a basis for a mechanistic analysis of their role.

molecular biology↗

bioRxiv2021

ModelRevelator: Fast phylogenetic model estimation via deep learning

Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, with discrimination between models determined by one of several information criteria. This requires tree reconstruction and optimisation which can be computationally expensive. We demonstrate that neural networks can be used to perform model selection, without the need to reconstruct trees, optimise parameters, or calculate likelihoods. We introduce ModelRevelator, a model selection tool underpinned by two deep neural networks. The first neural network, NNmodelfind, recommends one of six commonly used models of sequence evolution, ranging in complexity from Jukes and Cantor to General Time Reversible. The second, NNalphafind, recommends whether or not a{Gamma} --distributed rate heterogeneous model should be incorporated, and if so, provides an estimate of the shape parameter, . Users can simply input an MSA into ModelRevelator, and swiftly receive output recommending the evolutionary model, inclusive of the presence or absence of rate heterogeneity, and an estimate of . We show that ModelRevelator performs comparably with likelihood-based methods and the recently published machine learning method ModelTeller over a wide range of parameter settings, with significant potential savings in computational effort. Further, we show that this performance is not restricted to the alignments on which the networks were trained, but is maintained even on unseen empirical data. We expect that ModelRevelator will provide a valuable alternative for phylogeneticists, especially where traditional methods of model selection are computationally prohibitive.

evolutionary biology↗

bioRxiv2019

Distinguishing Felsenstein zone from Farris zone using neural networks

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently than the other. We show that a neural network can efficiently distinguish between four-taxon alignments that were evolved under conditions conducive to long-branch attraction, or long-branch repulsion. The feedback from the neural network can be used to select the most efficient tree reconstruction method yielding increased accuracy, when compared to a rigid choice of reconstruction methods. When applied to the contentious case of Strepsiptera evolution, our method agrees with the current scientific view.

evolutionary biology↗