The OGCleaner: filtering false-positive homology clusters.
Detecting homologous sequences in organisms is an essential step in protein structure and function prediction, gene annotation and phylogenetic tree construction. Heuristic methods are often employed for quality control of putative homology clusters. These heuristics, however, usually only apply to pairwise sequence comparison and do not examine clusters as a whole. We present the Orthology Group Cleaner (the OGCleaner), a tool designed for filtering putative orthology groups as homology or non-homology clusters by considering all sequences in a cluster. The OGCleaner relies on high-quality orthologous groups identified in OrthoDB to train machine learning algorithms that are able to distinguish between true-positive and false-positive homology groups. This package aims to improve the quality of phylogenetic tree construction especially in instances of lower-quality transcriptome assemblies.



https://github.com/byucsl/ogcleaner CONTACT: sfujimoto@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.
Computer Science Department, Brigham Young University, Provo, UT 84602, USA.
M Stanley
MS
Fujimoto
Department of Biology, Brigham Young University, Provo, UT 84602, USA.
Anton
A
Suvorov
Department of Biology, Brigham Young University, Provo, UT 84602, USA.
Nicholas O
NO
Jensen
Computer Science Department, Brigham Young University, Provo, UT 84602, USA.
Mark J
MJ
Clement
Computer Science Department, Brigham Young University, Provo, UT 84602, USA.
Quinn
Q
Snell
Department of Biology, Brigham Young University, Provo, UT 84602, USA.
Seth M
SM
Bybee
D000465:Algorithms; D058977:Molecular Sequence Annotation; D010802:Phylogeny; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins