CoCiter: An Efficient Tool to Infer Gene Function by Assessing the Significance of Literature Co-Citation

被引:32
作者
Qiao, Nan [1 ,2 ,3 ]
Huang, Yi [1 ,3 ]
Naveed, Hammad [1 ]
Green, Christopher D. [1 ]
Han, Jing-Dong J. [1 ]
机构
[1] Chinese Acad Sci, Shanghai Inst Biol Sci, Chinese Acad Sci Max Planck Partner Inst Computat, Chinese Acad Sci Key Lab Computat Biol, Shanghai, Peoples R China
[2] Chinese Acad Sci, Ctr Mol Syst Biol, Inst Genet & Dev Biol, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
来源
PLOS ONE | 2013年 / 8卷 / 09期
基金
中国国家自然科学基金;
关键词
INTEGRATIVE ANALYSIS; EXPRESSION DATA; NETWORK; ONTOLOGY;
D O I
10.1371/journal.pone.0074074
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A routine approach to inferring functions for a gene set is by using function enrichment analysis based on GO, KEGG or other curated terms and pathways. However, such analysis requires the existence of overlapping genes between the query gene set and those annotated by GO/KEGG. Furthermore, GO/KEGG databases only maintain a very restricted vocabulary. Here, we have developed a tool called "CoCiter" based on literature co-citations to address the limitations in conventional function enrichment analysis. Co-citation analysis is widely used in ranking articles and predicting protein-protein interactions (PPIs). Our algorithm can further assess the co-citation significance of a gene set with any other user-defined gene sets, or with free terms. We show that compared with the traditional approaches, CoCiter is a more accurate and flexible function enrichment analysis method. CoCiter is freely available at www.picb.ac.cn/hanlab/cociter/.
引用
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页数:8
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