Biology subjects

Knyshov, A.

Publications and source records attributed to Knyshov, A..

5 recordsLinked to original sources

bioRxiv2024

Predicting locus phylogenetic utility using machine learning

Disentangling evolutionary signal from noise in genomic datasets is essential to building phylogenies. The efficiency of current sequencing platforms and workflows has resulted in a plethora of large-scale phylogenomic datasets where, if signal is weak, it can be easily overwhelmed with non-phylogenetic signal and noise. However, the nature of the latter is not well understood. Although certain factors have been investigated and verified as impacting the accuracy of phylogenetic reconstructions, many others (as well as interactions among different factors) remain understudied. Here we use a large simulation-based dataset and machine learning to better understand the factors, and their interactions, that contribute to species tree error. We trained Random Forest regression models on the features extracted from simulated alignments under known phylogenies to predict the phylogenetic utility of the loci. Loci with the worst utility were then filtered out, resulting in an improved signal-to-noise ratio across the dataset. We investigated the relative importance of different features used by the model, as well as how they correspond to the originally simulated properties. We further used the model on several diverse empirical datasets to predict and subset the least reliable loci and re-infer the phylogenies. We measure the impacts of the subsetting on the overall topologies, difficult nodes identified in the original studies, as well as branch length distribution. Our results suggest that subsetting based on the utility predicted by the model can improve the topological accuracy of the trees and their average statistical support, and limits paralogy and its effects. Although the topology generated from the filtered datasets may not always be dramatically different from that generated from unfiltered data, the worst loci consistently yielded different topologies and worst statistical support, indicating that our protocol identified phylogenetic noise in the empirical data.

evolutionary biology↗

bioRxiv2023

Chromosome-aware phylogenomics of Assassin Bugs (Hemiptera: Reduvioidea) elucidates ancient gene conflict

Though the phylogenetic signal of loci on sex chromosomes can differ from those on autosomes, chromosomal-level genome assemblies for non-vertebrates are still relatively scarce and conservation of chromosomal gene content across deep phylogenetic scales has therefore remained largely unexplored. We here assemble a uniquely large and diverse set of samples (17 Anchored Hybrid Enrichment [AHE], 24 RNA-Seq, and 70 whole-genome sequencing [WGS] samples of variable depth) for the medically important assassin bugs (Reduvioidea). We assess the performance of genes based on multiple features (e.g., nucleotide vs. amino acid, nuclear vs. mitochondrial, and autosomal vs. X chromosomal) and employ different methods (concatenation and coalescence analyses) to reconstruct the unresolved phylogeny of this diverse ([~]7,000 spp.) and old (>180 MYA) group. Our results show that genes on the X chromosome are more likely to have discordant phylogenies than those on autosomes. We find that the X chromosome conflict is driven by high gene substitution rates that impact accuracy of phylogenetic inference. However, gene tree clustering showed strong conflict even after discounting variable third codon positions. Alternative topologies were not particularly enriched for sex chromosome loci, but spread across the genome. We conclude that binning genes to autosomal or sex chromosomes may result in a more accurate picture of the complex evolutionary history of a clade.

genomics↗

bioRxiv2021

Interrogating Genomic Data in the Phylogenetic Placement of Treeshrews Reveals Potential Sources of Conflict

The position of some taxa on the Tree of Life remains controversial despite the increase in genomic data used to infer phylogenies. While analyzing large datasets alleviates stochastic errors, it does not prevent systematic errors in inference, caused by both biological (e.g., incomplete lineage sorting, hybridization) and methodological (e.g., incorrect modeling, erroneous orthology assessments) factors. In this study, we systematically investigated factors that could result in these controversies, using the treeshrew (Scandentia, Mammalia) as a study case. Recent studies have narrowed the phylogenetic position of treeshrews to three competing hypotheses: sister to primates and flying lemurs (Primatomorpha), sister to rodents and lagomorphs (Glires), or sister to a clade comprising all of these. We sampled 50 mammal species including three treeshrews, a selection of taxa from the potential sister groups, and outgroups. Using a large diverse set of loci, we assessed support for the alternative phylogenetic position of treeshrews. The results suggest that the data has statistical support for two hypotheses for the placements of treeshrews, sister to Primatomorpha and to Primatomorpha+Glires. While we observe differences in properties of loci of different types (e.g., CDS, intron, etc.) with respect to the strength of the signal, the support for any particular topology is not dependent on the properties of the data. Rather, we show that the method of phylogenetic signal assessment, as well as whether the signal is measured using the full dataset or only loci with the strongest signal, impact the results much more.

evolutionary biology↗

bioRxiv2020

New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics

Despite many bioinformatic solutions for analyzing sequencing data, few options exist for targeted sequence retrieval from whole genomic sequencing (WGS) data. Available tools especially struggle at deep phylogenetic levels and necessitate amino-acid space searches, increasing rates of false positive results. Many such tools also suffer from difficult installation processes and lack adequate user resources. Here, we describe a program using freely available similarity search tools to find homologs in assembled WGS data with unparalleled freedom to modify parameters. We evaluate its performance as well as that of other utilized bioinformatics tools on two divergent insect species (>200 My) for which annotated genomes exist, as well as on one large set each of highly conserved and more variable loci. Our software is capable of retrieving orthologs from well-curated, low and high depth shotgun, and target capture assemblies as well or better than other software as assessed by finding the most genes with maximal coverage and with a low rate of false positives throughout all datasets. The software (implemented in Python), tutorials, and manual are freely available at https://github.com/AlexKnyshov/alibaseq.

bioinformatics↗

bioRxiv2018

Cost-efficient high throughput capture of museum arthropod specimen DNA using PCR-generated baits

O_LIGathering genetic data for rare species is one of the biggest remaining obstacles in modern phylogenetics, particularly for megadiverse groups such as arthropods. Next generation sequencing techniques allow for sequencing of short DNA fragments contained in preserved specimens >20 years old, but approaches such as whole genome sequencing are often too expensive for projects including many taxa. Several methods of reduced representation sequencing have been proposed that lower the cost of sequencing per specimen, but many remain costly because they involve synthesizing nucleotide probes and target hundreds of loci. These datasets are also frequently unique for each project and thus generally incompatible with other similar datasets.\nC_LIO_LIHere, we explore utilization of in-house generated DNA baits to capture commonly utilized mitochondrial and ribosomal DNA loci from insect museum specimens of various age and preservation types without the a priori need to know the sequence of the target loci. Both within species and cross-species capture are explored, on preserved specimens ranging in age from one to 54 years old.\nC_LIO_LIWe found most samples produced sufficient amounts of data to assemble the nuclear ribosomal rRNA genes and near complete mitochondrial genomes and produce well-resolved phylogenies in line with expected results. The dataset obtained can be straightforwardly combined with the large cache of existing Sanger-sequencing-generated data built up over the past 30 years and targeted loci can be easily modified to those commonly used in different taxa. Furthermore, the protocol we describe allows for inexpensive data generation (as low as [~]$35/sample), of at least 20 kilobases per specimen, for specimens at least as old as [~]1965, and can be easily conducted in most laboratories.\nC_LIO_LIIf widely applied, this technique will accelerate the accurate resolution of the Tree of Life especially on non-model organisms with limited existing genomic resources.\nC_LI

evolutionary biology↗