Biology subjects

Ivan, J.

Publications and source records attributed to Ivan, J..

3 recordsLinked to original sources

bioRxiv2026

PhyloRBT: A Phylogenetic Approach to Detect Reference Bias in Phylogenomic Datasets

Recent technological advancements have enabled the rapid generation of high-quality genomes across the tree of life, often resulting in multiple reference genomes for clades of phylogenetic interest. These reference genomes are often used to reconstruct phylogenetically informative loci from short-read data of newly-sequenced species. However, this approach can introduce reference bias where the reconstructed loci have erroneous similarities to those of the reference genome. Since reference bias can seriously affect downstream analyses, it is important to assess its presence in phylogenomic datasets. In this study, we propose PhyloRBT (phylogenetic reference bias test) to detect reference bias by reconstructing each locus multiple times using different reference genomes and then measuring the phylogenetic correlation between these reconstructions and the corresponding locus from the reference genomes. We applied PhyloRBT to hundreds of BUSCO loci reconstructed from short-read data of nine Eucalyptus species using 34 different reference genomes. Across the nine species, we found that more than a quarter of the reconstructed loci had significant evidence of reference bias. Excluding putatively biased loci from species tree inference resulted in a species tree topology that is more consistent with expectations from previous studies. In conclusion, PhyloRBT offers a straightforward way to detect reference bias in individual loci, and to selectively remove those biased loci from downstream analyses.

bioinformatics↗

bioRxiv2026

Using Variable Window Sizes for Phylogenomic Analyses of Whole Genome Alignments

AO_SCPLOWBSTRACTC_SCPLOWMany phylogenomic studies used non-overlapping windows to address gene tree discordance across a set of aligned genomes. Recently, Ivan et al. (2025) proposed an information theoretic approach to choose an optimal window size given the alignment. However, this approach selects only a single fixed window size per chromosome, which is a useful first step but fails to account for variation in the size of non-recombining regions along each chromosome. In this study, we extend the approach of Ivan et al. (2025) and propose PhyloNOW (phylogenomic non-overlapping windows) that allows window sizes to vary across the chromosome. We show that PhyloNOW outperforms the fixed-size approach on a wide range of simulated datasets. Applying the new method on two empirical datasets from Heliconius butterflies and great apes, we show that window sizes vary substantially across chromosomes. Our study highlights the limitations of using a fixed window size in non-overlapping window analyses, and proposes PhyloNOW that allows for variable window sizes across whole genome alignments. PhyloNOW is available at https://github.com/jeremiasivan/PhyloNOW.

bioinformatics↗

bioRxiv2025

Selecting a Window Size for the Analysis of Whole Genome Alignments using AIC

AO_SCPLOWBSTRACTC_SCPLOWThe variation of evolutionary histories along the genome presents a challenge for phylogenomic methods to identify the non-recombining regions and reconstruct the phylogenetic tree for each region. To address this problem, many studies used the non-overlapping window approach, often with an arbitrary selection of fixed window sizes that potentially include intra-window recombination events. In this study, we proposed an information theoretic approach to select a window size that best reflects the underlying histories of the alignment. First, we simulated chromosome alignments that reflected the key characteristics of an empirical dataset and found that the AIC is a good predictor of window size accuracy in correctly recovering the tree topologies of the alignment. Due to the issue of missing data in empirical datasets, we then designed a stepwise non-overlapping window approach and applied this method to the genomes of erato-sara Heliconius butterflies and great apes. We found that the best window sizes for the butterflies chromosomes ranged from <125bp to 250bp, which are much shorter than those used in a previous study even though this difference in window size did not significantly change the most common topologies across the genome. On the other hand, the best window sizes for great apes chromosomes ranged from 500bp to 1kb with the proportion of the major topology (grouping human and chimpanzee) falling between 60% and 87%, consistent with previous findings. Additionally, we observed a notable impact of stochastic error and concatenation when using small and large windows, respectively. For instance, the proportion of the major topology for great apes was 50% when using 250bp windows, but reached almost 100% for 64kb windows. In conclusion, our study highlights the challenges associated with selecting a window size in non-overlapping window analyses and proposes the AIC as a more objective way to select the optimal window size for whole genome alignments.

evolutionary biology↗