Constructing benchmark test sets for biological sequence analysis using independent set algorithms.

Autor: Samantha Petti, Sean R Eddy
Jazyk: angličtina
Rok vydání: 2022
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Zdroj: PLoS Computational Biology, Vol 18, Iss 3, p e1009492 (2022)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1009492
Popis: Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.
Databáze: Directory of Open Access Journals
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