GGRaSP: a R-package for selecting representative genomes using Gaussian mixture models
Author(s) -
Thomas H. Clarke,
Lauren Brinkac,
Granger Sutton,
Derrick E. Fouts
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/bty300
Subject(s) - genome , cluster analysis , selection (genetic algorithm) , computational biology , computer science , biology , bacterial genome size , r package , genomics , data mining , genetics , artificial intelligence , gene , computational science
The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom