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Constructing founder sets under allelic and non-allelic homologous recombination
Algorithms Mol Biol. 2023 Sep 29;18(1):15. doi: 10.1186/s13015-023-00241-3.ABSTRACTHomologous recombination between the maternal and paternal copies of a chromosome is a key mechanism for human inheritance and shapes population genetic properties of our species. However, a similar mechanism can also act between different copies of the same sequence, then called non-allelic homologous recombination (NAHR). This process can result in genomic rearrangements-including deletion, duplication, and inversion-and is underlying many genomic disorders. Despite its importance for genome evolution and disease, there is a lack of comput...
Source: Algorithms for Molecular Biology : AMB - September 29, 2023 Category: Molecular Biology Authors: Konstantinn Bonnet Tobias Marschall Daniel Doerr Source Type: research

Efficient gene orthology inference via large-scale rearrangements
Algorithms Mol Biol. 2023 Sep 28;18(1):14. doi: 10.1186/s13015-023-00238-y.ABSTRACTBACKGROUND: 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. Th...
Source: Algorithms for Molecular Biology : AMB - September 28, 2023 Category: Molecular Biology Authors: Diego P Rubert Mar ília D V Braga Source Type: research

Constructing phylogenetic networks via cherry picking and machine learning
CONCLUSIONS: Unlike the existing exact methods, our heuristics are applicable to datasets of practical size, and the experimental study we conducted on both simulated and real data shows that these solutions are qualitatively good, always within some small constant factor from the optimum. Moreover, our machine-learned heuristics are one of the first applications of machine learning to phylogenetics and show its promise.PMID:37717003 | PMC:PMC10505335 | DOI:10.1186/s13015-023-00233-3 (Source: Algorithms for Molecular Biology : AMB)
Source: Algorithms for Molecular Biology : AMB - September 16, 2023 Category: Molecular Biology Authors: Giulia Bernardini Leo van Iersel Esther Julien Leen Stougie Source Type: research

Constructing phylogenetic networks via cherry picking and machine learning
CONCLUSIONS: Unlike the existing exact methods, our heuristics are applicable to datasets of practical size, and the experimental study we conducted on both simulated and real data shows that these solutions are qualitatively good, always within some small constant factor from the optimum. Moreover, our machine-learned heuristics are one of the first applications of machine learning to phylogenetics and show its promise.PMID:37717003 | DOI:10.1186/s13015-023-00233-3 (Source: Algorithms for Molecular Biology : AMB)
Source: Algorithms for Molecular Biology : AMB - September 16, 2023 Category: Molecular Biology Authors: Giulia Bernardini Leo van Iersel Esther Julien Leen Stougie Source Type: research