Gene orthology inference via large-scale rearrangements for partially assembled genomes

Abstract

Background: Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. Although the ILP is quite efficient and could conceptually analyze genomes that are not completely assembled but split in several contigs, our tool failed in completing that task. The main reason is that each ILP pairwise comparison includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results: In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into m ≥ 1 subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on fruit fly genomes show two positive results. First, for complete assemblies the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining orthologies that are more similar to FlyBase orthologies when compared to orthologies computed by other inference tools. We added a post-processing for refining, with the aid of the mcl algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with mcl. We optimized several aspects of our implementation, achieving running times that are similar to the fastest alternative tools. Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded from our GitLab server at gitlab.ub.uni-bielefeld.de/gi/FFGC.

The Floor Is Lava: Halving Natural Genomes with Viaducts, Piers, and Pontoons

“…This is in contrast to similar solutions of other problems (e.g., Shao et al, 2015 or Bohnenkämper et al, 2021 ). The advantage is that the size of our ILP is linear with respect to the number of linear chromosomes, while solutions relying on capping grow quadratically in the number of linear chromosomes, which leads to a dramatic increase in the search space (Rubert and Braga, 2022 ).…”

Pangenome calculation beyond the species level using RIBAP: A comprehensive bacterial core genome annotation pipeline based on Roary and pairwise ILPs

“…Running the same dataset on an HPC with the preconfigured SLURM profile reduces the runtime to 1 h. (Bohnenkämper et al 2021) proposed an extension of the original ILP by Shao et al (Shao, Lin, and Moret 2015) that enables rearrangement analysis of genomes without imposing further restrictions. Expanding from this, Rubert et al (Rubert, Martinez, and Braga 2021;Rubert and Braga 2022) further adapted this model to allow gene family-free analysis of pairwise genomes. Our analysis did not seem limited by the naive ILP model involved.…”