SCIENCE CHINA Information Sciences, Volume 59 , Issue 7 : 070108(2016) https://doi.org/10.1007/s11432-016-5581-1

GOAL: the comprehensive gene \\ontology analysis layer

More info
  • ReceivedApr 5, 2016
  • AcceptedApr 30, 2016
  • PublishedJun 13, 2016


Funded by

National Science Foundation(Award OIA-1028098)



Chen X was supported by National Science Foundation (Award OIA-1028098).


[1] Patel A P, Tirosh I, Trombetta J J, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 2014, 344: 1396-1401 CrossRef Google Scholar

[2] Ploper D, Taelman V F, Robert L, et al. MITF drives endolysosomal biogenesis and potentiates Wnt signaling in melanoma cells. Proc Nat Acad Sci USA, 2015. 112: E420--E429. Google Scholar

[3] Ashburner M, Ball C A, Blake J A, et al. Gene ontology: tool for the unification of biology. Nat Genet, 2000, 25: 25-29 CrossRef Google Scholar

[4] Salzman J, Chen R E, Olsen M N, et al. Cell-type specific features of circular RNA expression. PLoS Genet, 2013, 9: e1003777-29 CrossRef Google Scholar

[5] Caffrey C R, Rohwer A, Oellien F, et al. A comparative chemogenomics strategy to predict potential drug targets in the metazoan pathogen, Schistosoma mansoni. PLoS ONE, 2009, 4: e4413-29 CrossRef Google Scholar

[6] Campillos M, Kuhn M, Gavin A C, et al. Drug target identification using side-effect similarity. Science, 2008, 321: 263-266 CrossRef Google Scholar

[7] Crowther G J, Shanmugam D, Carmona S J, et al. Identification of attractive drug targets in neglected-disease pathogens using an in silico approach. PLoS Negl Trop Dis, 2010, 4: e804-266 CrossRef Google Scholar

[8] Smith C. Drug target identification: a question of biology. Nature, 2004, 428: 225-231 Google Scholar

[9] Takenaka T. Classical vs reverse pharmacology in drug discovery. BJU Int, 2001, 88, Suppl 2: 7--10; discussion 49--50. Google Scholar

[10] Osadchy M, Kolodny R. Maps of protein structure space reveal a fundamental relationship between protein structure and function. Proc Nat Acad Sci USA, 2011, 108: 12301-12306 CrossRef Google Scholar

[11] Yildirim M A, Goh K I, Cusick M E, et al. Drug-target network. Nat Biotechnol, 2007, 25: 1119-1126 CrossRef Google Scholar

[12] Devos D, Valencia A. Intrinsic errors in genome annotation. Trends Genet, 2001, 17: 429-431 CrossRef Google Scholar

[13] Petrey D, Fischer M, Honig B. Structural relationships among proteins with different global topologies and their implications for function annotation strategies. Proc Nat Acad Sci USA, 2009, 106: 17377-17382 CrossRef Google Scholar

[14] Yu H Y, Luscombe N M, Lu H X, et al. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genom Res, 2004, 14: 1107-1118 CrossRef Google Scholar

[15] Petrey D, Honig B. Is protein classification necessary? Toward alternative approaches to function annotation. Curr Opin Struct Biol, 2009, 19: 363-368 CrossRef Google Scholar

[16] Jeong J C, Chen X-W. Evaluating topology-based metrics for GO term similarity measures. In: Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, 2013. 43--48. Google Scholar

[17] Gentleman R. Visualizing and distances using GO. 2010. \url{http://www.bioconductor.org/packages/release/bioc/vignettes/GOstats/inst/doc/GOvis.pdf}. Google Scholar

[18] Jiang J J, Conrath D W. Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference Research on Computational Linguistics (ROCLING X), Taipei, 1997. Google Scholar

[19] Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco: Morgan Kaufmann Publishers Inc., 1995. 448--453. Google Scholar

[20] Schlicker A, Domingues F S, Rahnenführer J, et al. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinform, 2006, 7: 302-368 CrossRef Google Scholar

[21] Ye P, Peyser B D, Pan X, et al. Gene function prediction from congruent synthetic lethal interactions in yeast. Mol Syst Biol, 2005, 1: 2005-368 Google Scholar

[22] Lerman G, Shakhnovich B E. Defining functional distance using manifold embeddings of gene ontology annotations. Proc Nat Acad Sci USA, 2007, 104: 11334-11339 CrossRef Google Scholar

[23] Lin D. An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning. San Francisco: Morgan Kaufmann Publishers Inc., 1998. 296--304. Google Scholar

[24] Shannon C E. The mathematical theory of communication. 1963. MD Comput, 1997, 14: 306-317 Google Scholar

[25] Jeong J C, Chen X W. A new semantic functional similarity over gene ontology. IEEE/ACM Trans Comput Biol Bioinform, 2014, 12: 322-334 Google Scholar

[26] Chen X W, Jeong J C, Dermyer P. KUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions. Nucl Acids Res, 2011, 39: 750-754 CrossRef Google Scholar

[27] Andreeva A, Howorth D, Chandonia J M, et al. Data growth and its impact on the SCOP database: new developments. Nucl Acids Res, 2008, 36: D419-D425 Google Scholar

[28] Orengo C A, Michie A D, Jones S, et al. CATH---a hierarchic classification of protein domain structures. Structure, 1997, 5: 1093-1108 CrossRef Google Scholar

[29] Consortium T U. The Universal Protein Resource (UniProt) in 2010. Nucl Acids Res, 2010, 38: D142-D148 CrossRef Google Scholar

[30] Lord P W, Stevens R D, Brass A, et al. Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics, 2003, 19: 1275-1283 CrossRef Google Scholar

[31] Schlicker A, Albrecht M. FunSimMat: a comprehensive functional similarity database. Nucl Acids Res, 2008, 36: D434-D439 Google Scholar

[32] Pesquita C, Faria D, Bastos H, et al. Metrics for GO based protein semantic similarity: a systematic evaluation. BMC Bioinform, 2008, 9, Suppl 5: S4-D439 Google Scholar

[33] Wang J Z, Du Z, Payattakool R, et al. A new method to measure the semantic similarity of GO terms. Bioinformatics, 2007, 23: 1274-1281 CrossRef Google Scholar

[34] Hamosh A, Scott A F, Amberger J S, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucl Acids Res, 2005, 33: D514-D517 Google Scholar

[35] Schlicker A, Lengauer T, Albrecht M. Improving disease gene prioritization using the semantic similarity of Gene Ontology terms. Bioinformatics, 2010, 26: i561-i567 CrossRef Google Scholar


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