Biology subjects

Swenson, K. M.

Publications and source records attributed to Swenson, K. M..

3 recordsLinked to original sources

bioRxiv2025

Identifying Positional Orthologs by Linear Programming

Studying the evolution of gene orders is essential to our understanding of the link between gene regulation and phenotype. Comparative studies concerning the full complement of genes in large numbers of genomes are now possible, thanks to the advent of affordable long-read sequencing and whole chromosome assembly, combined with automated genome annotation. The study of gene orders remains cumbersome, however, due to a lack of a streamlined, and standardized, process meant for the identification of positional orthologs. Within a family of homologous genes, these are those pairs of orthologous gene copies that descend from the same locus in their most recent ancestor, not having been produced by an intervening duplication event. This article approaches the detection of positional orthologs by way of the MAXIMUM COLORFUL GRAPH PARTITION Problem. We present two novel integer linear programming solutions to this problem, evaluate their efficacy on simulated and real data, while comparing them to several new and existing heuristics, as well as to the Double Cut and Join based ILP called FFGC. Digital Object Identifier10.4230/LIPIcs.CVIT.2016.23

bioinformatics↗

bioRxiv2022

Evaluating Impacts of Syntenic Block Detection Strategies on Rearrangement Phylogeny Using Mycobacterium tuberculosis Isolates

AO_SCPLOWBSTRACTC_SCPLOWPhylogenetic inference based on genomic structural variations, that manipulate the gene order and content of whole chromosomes, promises to inform a more comprehensive understanding of evolution. The first challenge in using such data, the incompleteness of available de novo assemblies, is easing as long read technologies enable (near-)complete genome assembly, but methodological challenges remain. To obtain the input to rearrangement-based inference methods, we need to detect syntenic blocks of orthologous sequences, a task that can be accomplished in many ways, none of which are obviously preferable. In this paper, we use 94 reference quality genomes of primarily Mycobacterium tuberculosis (Mtb) isolates as a benchmark to evaluate these methods. The clonal nature of Mtb evolution, the manageable genome sizes, along with substantial levels of structural variation make this an ideal benchmarking dataset. We test several methods for detecting homology and obtaining syntenic blocks, and two methods for inferring phylogenies, comparing them to the standard method that uses substitutions for inferring the tree. We find that not only the choice of methods but also their parameters can impact results, especially among branches with lower support. In particular, a method based on an encoding of adjacencies applied to Cactus-defined blocks was fully compatible with the highly supported branches of the substitution-based tree. Thus, we were able to combine the two trees to obtain a supertree with high resolution utilizing both SNPs and rearrangements. Furthermore, we observed that the results were much less affected by the choice of the tree inference method than by the method used to determine the underlying syntenic blocks. Overall, our results indicate that accurate trees can be inferred using genome rearrangements, but the choice of the methods for inferring the homology matters and requires care.

genomics↗

bioRxiv2017

Rearrangement Scenarios Guided By Chromatin Structure

Genome architecture can be drastically modified through a succession of large-scale rearrangements. In the quest to infer accurate ancestral rearrangement scenarios, it is often the case that parsimony principal alone does not impose enough constraints. Thus, the current challenge is to consider more biological information in the inference process. In previous work, we introduced a model for such a task, based on a partition into equivalence classes of the adjacencies between genes. Such a partition is amenable to the representation of spacial constraints as given by Hi-C data. A major open question is the validity of such a model. In this note, we show that the quality of a clustering of the adjacencies based on Hi-C data is directly correlated to the quality of a rearrangement scenario that we compute between Drosophila melanogaster and D. yakuba.

genomics↗