KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold

被引：1270

作者：

Aramaki, Takuya ^{[1
]}

Blanc-Mathieu, Romain ^{[1
]}

Endo, Hisashi ^{[1
]}

Ohkubo, Koichi ^{[1
,2
]}

Kanehisa, Minoru ^{[1
]}

Goto, Susumu ^{[3
]}

Ogata, Hiroyuki ^{[1
]}

机构：

[1] Kyoto Univ, Inst Chem Res, Bioinformat Ctr, Uji, Kyoto 6110011, Japan

[2] Hewlett Packard Japan Ltd, Koto Ku, Tokyo 1368711, Japan

[3] Res Org Informat & Syst, Database Ctr Life Sci, Kashiwa, Chiba 2770871, Japan

来源：

BIOINFORMATICS | 2020年 / 36卷 / 07期

关键词：

GENERATION;

D O I：

10.1093/bioinformatics/btz859

中图分类号：

Q5 [生物化学];

学科分类号：

070307 [化学生物学];

摘要：

The Summary: KofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction.

引用

页码：2251 / 2252

页数：2

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