Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences
Author(s) -
Aashish Jain,
Daisuke Kihara
Publication year - 2018
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/bty704
Subject(s) - annotation , function (biology) , similarity (geometry) , computational biology , sequence alignment , phylogenetic tree , protein function prediction , biology , genomics , sequence (biology) , genome , protein function , multiple sequence alignment , comparative genomics , computer science , bioinformatics , genetics , artificial intelligence , gene , peptide sequence , image (mathematics)
Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation.
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