Open AccessConstructing benchmark test sets for biological sequence analysis using independent set algorithms
Author(s) -
Samantha Petti,
Sean R. Eddy
Publication year - 2022
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009492
Subject(s) - sequence (biology) , benchmark (surveying) , algorithm , set (abstract data type) , benchmarking , computer science , test set , alignment free sequence analysis , sequence analysis , multiple sequence alignment , sequence alignment , graph , test (biology) , mathematics , artificial intelligence , theoretical computer science , biology , genetics , peptide sequence , marketing , gene , business , programming language , geography , paleontology , geodesy
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.