SCIENCE CHINA Information Sciences, Volume 59 , Issue 7 : 070101(2016) https://doi.org/10.1007/s11432-016-5584-y

Integrating phenotypic features and tissue-specific information to prioritize disease genes

More info
  • ReceivedMar 31, 2016
  • AcceptedApr 18, 2016
  • PublishedJun 6, 2016


Funded by

National Natural Science Foundation of China(61532014)

National Natural Science Foundation of China(91530113)

National Natural Science Foundation of China(61432010)

National Natural Science Foundation of China(61402349)

National Natural Science Foundation of China(61303122)

National Natural Science Foundation of China(61303118)

Fundamental Research Funds for the Central Universities(BDZ021404)



This work was supported by National Natural Science Foundation of China (Grant Nos. 61532014, 91530113, 61432010, 61402349, 61303122, 61303118) and Fundamental Research Funds for the Central Universities (Grant No. BDZ021404).


[1] Ritchie M D, Holzinger E R, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet, 2015, 16: 85-97 CrossRef Google Scholar

[2] Moreau Y, Tranchevent L-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet, 2012, 13: 523-536 CrossRef Google Scholar

[3] Piro R M, Di Cunto F. Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J, 2012, 279: 678-696 CrossRef Google Scholar

[4] Wang X J, Gulbahce N, Yu H Y. Network-based methods for human disease gene prediction. Brief Funct Genomics, 2011, 10: 280-293 CrossRef Google Scholar

[5] Lan W, Wang J X, Li M, et al. Computational approaches for prioritizing candidate disease genes based on PPI networks. Tsinghua Sci Technol, 2015, 20: 500-512 CrossRef Google Scholar

[6] Wu X B, Jiang R, Zhang M, et al. Network-based global inference of human disease genes. Mol Syst Biol, 2008, 4: 189-512 Google Scholar

[7] Vanunu O, Magger O, Ruppin E, et al. Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol, 2010, 6: e1000641-512 CrossRef Google Scholar

[8] Li Y J, Patra J C. Genome-wide inferring gene-phenotype relationship by walking on the heterogeneous network. Bioinformatics, 2010, 26: 1219-1224 CrossRef Google Scholar

[9] Wang J X, Peng X Q, Peng W, et al. Dynamic protein interaction network construction and applications. Proteomics, 2014, 14: 338-352 CrossRef Google Scholar

[10] Gaulton K J, Mohlke K L, Vision T J. A computational system to select candidate genes for complex human traits. Bioinformatics, 2007, 23: 1132-1140 CrossRef Google Scholar

[11] 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

[12] Linghu B, Snitkin E S, Hu Z, et al. Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol, 2009, 10: R91-i567 CrossRef Google Scholar

[13] Franke L, van Bakel H, Fokkens L, et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Amer J Hum Genet, 2006, 78: 1011-1025 CrossRef Google Scholar

[14] Robinson P N, Webber C. Phenotype ontologies and cross-species analysis for translational research. PLoS Genet, 2014, 10: e1004268-1025 CrossRef Google Scholar

[15] Hwang S, Kim E, Yang S, et al. MORPHIN: a web tool for human disease research by projecting model organism biology onto a human integrated gene network. Nucl Acids Res, 2014, 42: W147-W153 CrossRef Google Scholar

[16] Winter E E, Goodstadt L, Ponting C P. Elevated rates of protein secretion, evolution, and disease among tissue-specific genes. Genome Res, 2004, 14: 54-61 Google Scholar

[17] Chao E C, Lipkin S M. Molecular models for the tissue specificity of DNA mismatch repair-deficient carcinogenesis. Nucl Acids Res, 2006, 34: 840-852 CrossRef Google Scholar

[18] Magger O, Waldman Y Y, Ruppin E, et al. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput Biol, 2012, 8: e1002690-852 CrossRef Google Scholar

[19] Prasad T S K, Goel R, Kandasamy K, et al. Human protein reference database-2009 update. Nucl Acids Res, 2009, 37: D767-D772 CrossRef Google Scholar

[20] Barshir R, Basha O, Eluk A, et al. The tissuenet database of human tissue protein-protein interactions. Nucl Acids Res, 2013, 41: D841-D844 CrossRef Google Scholar

[21] Su A I, Wiltshire T, Batalov S, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Nat Acad Sci USA, 2004, 101: 6062-6067 CrossRef Google Scholar

[22] Berglund L, Björling E, Oksvold P, et al. A genecentric human protein atlas for expression profiles based on antibodies. Mol Cell Proteom, 2008, 7: 2019-2027 CrossRef Google Scholar

[23] Bradley R K, Merkin J, Lambert N J, et al. Alternative splicing of RNA triplets is often regulated and accelerates proteome evolution. PLoS Biol, 2012, 10: e1001229-2027 CrossRef Google Scholar

[24] Chatr-aryamontri A, Breitkreutz B-J, Oughtred R, et al. The BioGRID interaction database: 2015 update. Nucl Acids Res, 2015, 43: D470-D478 CrossRef Google Scholar

[25] Salwinski L, Miller C S, Smith A J, et al. The database of interacting proteins: 2004 update. Nucl Acids Res, 2004, 32: D449-D451 CrossRef Google Scholar

[26] Orchard S, Ammari M, Aranda B, et al. The MIntAct project---IntAct as a common curation platform for 11 molecular interaction databases. Nucl Acids Res, 2014, 42: D358-D363 CrossRef Google Scholar

[27] Licata L, Briganti L, Peluso D, et al. MINT, the molecular interaction database: 2012 update. Nucl Acids Res, 2012, 40: D857-D861 CrossRef Google Scholar

[28] Barshir R, Shwartz O, Smoly I Y, et al. Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput Biol, 2014, 10: e1003632-D861 CrossRef Google Scholar

[29] Greene C S, Krishnan A, Wong A K, et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet, 2015, 47: 569-576 CrossRef Google Scholar

[30] Li M, Zhang J Y, Liu Q, et al. Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation. BMC Med Genomics, 2014, 7: S4-576 Google Scholar

[31] Ganegoda G U, Wang J X, Wu F-X, et al. Prediction of disease genes using tissue-specified gene-gene network. BMC Syst Biol, 2014, 8: S3-576 Google Scholar

[32] Jacquemin T, Jiang R. Walking on a tissue-specific disease-protein-complex heterogeneous network for the discovery of disease-related protein complexes. BioMed Res Int, 2013, 2013: 455-458 Google Scholar

[33] Robinson P, Krawitz P, Mundlos S. Strategies for exome and genome sequence data analysis in disease-gene discovery projects. Clin Genet, 2011, 80: 127-132 CrossRef Google Scholar

[34] Köhler S, Bauer S, Horn D, et al. Walking the interactome for prioritization of candidate disease genes. Amer J Hum Genet, 2008, 82: 949-958 CrossRef Google Scholar

[35] van Driel M A, Bruggeman J, Vriend G, et al. A text-mining analysis of the human phenome. Eur J Hum Genet, 2006, 14: 535-542 CrossRef Google Scholar

[36] Brunner H G, van Driel M A. From syndrome families to functional genomics. Nat Rev Genet, 2004, 5: 545-551 CrossRef Google Scholar

[37] Yang H, Robinson P N, Wang K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat Methods, 2015, 12: 841-843 CrossRef Google Scholar

[38] Javed A, Agrawal S, Ng P C. Phen-Gen: combining phenotype and genotype to analyze rare disorders. Nat Methods, 2014, 11: 935-937 CrossRef Google Scholar

[39] Chen Y, Jiang T, Jiang R. Uncover disease genes by maximizing information flow in the phenome-interactome network. Bioinformatics, 2011, 27: i167-i176 CrossRef Google Scholar

[40] Xie M Q, Hwang T, Kuang R. Prioritizing disease genes by bi-random walk. In: Proceedings of 16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, Kuala Lumpur, 2012. 292--303. Google Scholar

[41] 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

[42] Lage K, Hansen N T, Karlberg E O, et al. A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Nat Acad Sci, 2008, 105: 20870-20875 CrossRef Google Scholar

[43] Basha O, Flom D, Barshir R, et al. MyProteinNet: build up-to-date protein interaction networks for organisms, tissues and user-defined contexts. Nucl Acids Res, 2015, 43: W258-W263 CrossRef Google Scholar

[44] Köhler S, Doelken S C, Mungall C J, et al. The human phenotype ontology project: linking molecular biology and disease through phenotype data. Nucl Acids Res, 2014, 42: D966-D974 CrossRef Google Scholar

[45] 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

[46] Schlicker A, Domingues F, Rahnenführer J, et al. A new measure for functional similarity of gene products based on Gene Ontology. BMC Bioinform, 2006, 7: 302-D974 CrossRef Google Scholar

[47] Guo X L, Gao L, Wei C S, et al. A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PLoS ONE, 2011, 6: e24171-D974 CrossRef Google Scholar

[48] Zhou X Z, Menche J, Barabási A-L, et al. Human symptoms-disease network. Nat Commun, 2014, 5: 4212-D974 Google Scholar

[49] Goh K-I, Cusick M E, Valle D, et al. The human disease network. Proc Nat Acad Sci, 2007, 104: 8685-8690 CrossRef Google Scholar


Contact and support