Open AccessUsing de novo protein structure predictions to measure the quality of very large multiple sequence alignments
Author(s) -
Gearóid Fox,
Fabian Sievers,
Desmond G. Higgins
Publication year - 2015
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btv592
Subject(s) - benchmark (surveying) , computer science , multiple sequence alignment , sequence (biology) , data mining , scripting language , sequence alignment , rank (graph theory) , computational biology , biology , peptide sequence , programming language , mathematics , biochemistry , genetics , geodesy , gene , geography , combinatorics
Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data.