GraphPart: homology partitioning for biological sequence analysis.
| Autor: | Teufel F; Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.; Digital Science & Innovation, Novo Nordisk A/S, 2760 Måløv, Denmark., Gíslason MH; Department of Genomic Medicine, Copenhagen University Hospital/Rigshospitalet, 2100 Copenhagen, Denmark., Almagro Armenteros JJ; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA.; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA., Johansen AR; Department of Computer Science, Stanford University School of Engineering, Stanford, CA 94305, USA., Winther O; Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark.; Department of Genomic Medicine, Copenhagen University Hospital/Rigshospitalet, 2100 Copenhagen, Denmark.; Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark., Nielsen H; Department of Health Technology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark. |
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| Jazyk: | angličtina |
| Zdroj: | NAR genomics and bioinformatics [NAR Genom Bioinform] 2023 Oct 16; Vol. 5 (4), pp. lqad088. Date of Electronic Publication: 2023 Oct 16 (Print Publication: 2023). |
| DOI: | 10.1093/nargab/lqad088 |
| Abstrakt: | When splitting biological sequence data for the development and testing of predictive models, it is necessary to avoid too-closely related pairs of sequences ending up in different partitions. If this is ignored, performance of prediction methods will tend to be overestimated. Several algorithms have been proposed for homology reduction, where sequences are removed until no too-closely related pairs remain. We present GraphPart, an algorithm for homology partitioning that divides the data such that closely related sequences always end up in the same partition, while keeping as many sequences as possible in the dataset. Evaluation of GraphPart on Protein, DNA and RNA datasets shows that it is capable of retaining a larger number of sequences per dataset, while providing homology separation on a par with reduction approaches. (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.) |
| Databáze: | MEDLINE |
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