%0 Journal Article %1 kanehisa2016blastkoala %A Kanehisa, Minoru %A Sato, Yoko %A Morishima, Kanae %D 2016 %J Journal of Molecular Biology %K 10 algorithms kegg methods %N 4 %P 726-731 %R 10.1016/j.jmb.2015.11.006 %T BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences %U http://www.sciencedirect.com/science/article/pii/S002228361500649X?via%3Dihub %V 428 %X BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.

@article{kanehisa2016blastkoala, abstract = {BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.}, added-at = {2017-11-09T15:58:54.000+0100}, author = {Kanehisa, Minoru and Sato, Yoko and Morishima, Kanae}, biburl = {https://www.bibsonomy.org/bibtex/2727719b31612657fa652411d99969c6f/artheibault}, description = {This article describes the algorithms BlastKOALA and GhostKOALA, which are employed by the KO database to compare gene and protein data. Both are based on the Smith-Waterman algorithm. BlastKOALA uses BLASTP and is slower but more precise, whereas GhostKOALA uses GHOSTX and is faster but less precise.}, doi = {10.1016/j.jmb.2015.11.006}, interhash = {0fc1aa8a7084e0de60cac1762b73d1b7}, intrahash = {727719b31612657fa652411d99969c6f}, journal = {Journal of Molecular Biology}, keywords = {10 algorithms kegg methods}, month = feb, number = 4, pages = {726-731}, timestamp = {2017-11-09T15:58:54.000+0100}, title = {BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences}, url = {http://www.sciencedirect.com/science/article/pii/S002228361500649X?via%3Dihub}, volume = 428, year = 2016 }