The potential of family-free rearrangements towards gene orthology inference

Rubert, Diego P.; Doerr, Daniel; Braga, Marília D. V.

doi:10.1142/s021972002140014x

Cited by 5 publications

(8 citation statements)

References 26 publications

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“…Furthermore, we are confident that using this new formula, capping-free versions of other existing algorithms, such as for the family-free distance problem as in [ 8 , 18 ] and parsimony problems as in [ 19 ] can be devised.…”

Section: Discussionmentioning

confidence: 99%

Recombinations, chains and caps: resolving problems with the DCJ-indel model

Bohnenkämper

2024

Algorithms Mol Biol

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One of the most fundamental problems in genome rearrangement studies is the (genomic) distance problem. It is typically formulated as finding the minimum number of rearrangements under a model that are needed to transform one genome into the other. A powerful multi-chromosomal model is the Double Cut and Join (DCJ) model.While the DCJ model is not able to deal with some situations that occur in practice, like duplicated or lost regions, it was extended over time to handle these cases. First, it was extended to the DCJ-indel model, solving the issue of lost markers. Later ILP-solutions for so called natural genomes, in which each genomic region may occur an arbitrary number of times, were developed, enabling in theory to solve the distance problem for any pair of genomes. However, some theoretical and practical issues remained unsolved. On the theoretical side of things, there exist two disparate views of the DCJ-indel model, motivated in the same way, but with different conceptualizations that could not be reconciled so far. On the practical side, while ILP solutions for natural genomes typically perform well on telomere to telomere resolved genomes, they have been shown in recent years to quickly loose performance on genomes with a large number of contigs or linear chromosomes. This has been linked to a particular technique, namely capping. Simply put, capping circularizes linear chromosomes by concatenating them during solving time, increasing the solution space of the ILP superexponentially. Recently, we introduced a new conceptualization of the DCJ-indel model within the context of another rearrangement problem. In this manuscript, we will apply this new conceptualization to the distance problem. In doing this, we uncover the relation between the disparate conceptualizations of the DCJ-indel model. We are also able to derive an ILP solution to the distance problem that does not rely on capping. This solution significantly improves upon the performance of previous solutions on genomes with high numbers of contigs while still solving the problem exactly and being competitive in performance otherwise. We demonstrate the performance advantage on simulated genomes as well as showing its practical usefulness in an analysis of 11 Drosophila genomes.

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Section: Discussionmentioning

confidence: 99%

Recombinations, chains and caps: resolving problems with the DCJ-indel model

Bohnenkämper

2024

Algorithms Mol Biol

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“…We devised and implemented a heuristic capping for improving our recently developed pipeline OrthoFFGC [13] for inferring gene families based on genome rearrangements. In OrthoFFGC we adopted an optimal capping including all connec-tions between the ends of linear segments to allow all possible (2p * )!…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Integration of pairwise optimal ortholog-sets into gene families In our previous work [13], the ILP FF-DCJ-Indel solving OrthoFF (with optimal capping) was integrated in a tool called OrthoFFGC for inferring gene families across several species. The pipeline can be summarized as follows: given a set of n genomes, gene similarities and ortholog-sets are computed for all pairwise comparisons and simply integrated into an n-partite graph.…”

Section: Optimally Capped Family-free Relational Graphmentioning

confidence: 99%

“…With these achievements we were able to invert the traditional paradigm of genome rearrangement studies: instead of requiring the gene families to proceed with rearrangement comparisons, it became possible to use rearrangement comparisons for inferring the gene families [1] . Indeed, in our previous work [13], we did a first attempt of using FF-DCJ-Indel for inferring genome-scale gene families across several species. More precisely, given a set of genomes, our method first computes all pairwise optimal gene matchings, which are integrated into gene families in the second step, resulting in a complete pipeline called OrthoFFGC [2] , whose inferences displayed good quality in the analysis of completely assembled genomes.…”

Section: Introductionmentioning

confidence: 99%

“…The tool msoar was not maintained and is no longer operational, therefore we could never compare its performance to FF-DCJ-Indel. [2] Our method was originally called DiffMGC [13], here renamed to OrthoFFGC. segments be connected.…”

Section: Introductionmentioning

confidence: 99%

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

Rubert

Braga

2023

Preprint

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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.

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