期刊论文详细信息
BMC Bioinformatics
PhenUMA: a tool for integrating the biomedical relationships among genes and diseases
Rocío Rodríguez-López1  Armando Reyes-Palomares1  Francisca Sánchez-Jiménez1  Miguel Ángel Medina1 
[1] CIBER de Enfermedades Raras (CIBERER), Málaga, E-29071, Spain
关键词: Network biology;    Network medicine;    Systems biology;    Gene-disease relationships;    Phenotypic relationships;    Functional relationships;   
Others  :  1084894
DOI  :  10.1186/s12859-014-0375-1
 received in 2014-04-21, accepted in 2014-11-04,  发布年份 2014
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【 摘 要 】

Background

Several types of genetic interactions in humans can be directly or indirectly associated with the causal effects of mutations. These interactions are usually based on their co-associations to biological processes, coexistence in cellular locations, coexpression in cell lines, physical interactions and so on. In addition, pathological processes can present similar phenotypes that have mutations either in the same genomic location or in different genomic regions. Therefore, integrative resources for all of these complex interactions can help us prioritize the relationships between genes and diseases that are most deserving to be studied by researchers and physicians.

Results

PhenUMA is a web application that displays biological networks using information from biomedical and biomolecular data repositories. One of its most innovative features is to combine the benefits of semantic similarity methods with the information taken from databases of genetic diseases and biological interactions. More specifically, this tool is useful in studying novel pathological relationships between functionally related genes, merging diseases into clusters that share specific phenotypes or finding diseases related to reported phenotypes.

Conclusions

This framework builds, analyzes and visualizes networks based on both functional and phenotypic relationships. The integration of this information helps in the discovery of alternative pathological roles of genes, biological functions and diseases. PhenUMA represents an advancement toward the use of new technologies for genomics and personalized medicine.

【 授权许可】

   
2014 Rodríguez-López et al; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Robinson PN: Deep phenotyping for precision medicine. Hum Mutat 2012, 33:777-780.
  • [2]Girdea M, Dumitriu S, Fiume M, Bowdin S, Boycott KM, Chénier S, Chitayat D, Faghfoury H, Meyn MS, Ray PN, So J, Stavropoulos DJ, Brudno M: PhenoTips: patient phenotyping software for clinical and research use. Hum Mutat 2013, 34:1057-1065.
  • [3]Hamosh A, Sobreira N, Hoover-Fong J, Sutton VR, Boehm C, Schiettecatte F, Valle D: PhenoDB: a new web-based tool for the collection, storage, and analysis of phenotypic features. Hum Mutat 2013, 34:566-571.
  • [4]Schofield PN, Hancock JM: Integration of global resources for human genetic variation and disease. Hum Mutat 2012, 33:813-816.
  • [5]Baker M: Big biology: the’omes puzzle. Nature 2013, 494:416-419.
  • [6][http://www.omim.org] webcite Online Mendelian Inheritance in Man, OMIM®. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University (Baltimore, MD) []
  • [7][http://www.orpha.net] webcite Orphanet: an online rare disease and orphan drug data base. © INSERM 1997 []
  • [8]Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S: Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 2012, 33:803-808.
  • [9]Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, Rajan D, Van Vooren S, Moreau Y, Pettett RM, Carter NP: REPORT DECIPHER: database of chromosomal imbalance and phenotype in humans using ensembl resources. Am J Hum Genet 2009, 84:524-533.
  • [10]Robinson PN, Mundlos S: The human phenotype ontology. Clin Genet 2010, 77:525-534.
  • [11]Mistry M, Pavlidis P: Gene ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics 2008, 9:327. BioMed Central Full Text
  • [12]Vidal M, Cusick ME, Barabási A-L: Interactome networks and human disease. Cell 2011, 144:986-998.
  • [13]Reyes-Palomares A, Rodríguez-López R, Ranea JAG, Sánchez Jiménez F, Medina MA: Global analysis of the human pathophenotypic similarity gene network merges disease module components. PLoS One 2013, 8:e56653.
  • [14]Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011, 39(Database issue):D561-D568.
  • [15]Veeramani B, Bader JS: Metabolic flux correlations, genetic interactions, and disease. J Comput Biol 2009, 16:291-302.
  • [16]Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics 2010, 26:2347-2348.
  • [17][http://www.orphadata.org] webcite Orphadata: Free access data from Orphanet. © INSERM 1997 []
  • [18]Bauer S, Grossmann S, Vingron M, Robinson PN: Ontologizer 2. 0 — a multifunctional tool for GO term enrichment analysis and data exploration. Bioinformatics 2008, 24:1650-1651.
  • [19]Resnik P: Using information content to evaluate semantic similarity in a taxonomy. IJCAI 1995, 1:448-453.
  • [20]Lord PW, Stevens RD, Brass A, Goble CA: Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 2003, 19:1275-1283.
  • [21]Xu T, Du L, Zhou Y: Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data. BMC Bioinformatics 2008, 9:472. BioMed Central Full Text
  • [22]Sevilla JL, Segura V, Podhorski A, Guruceaga E, Mato JM, Martinez-Cruz LA, Corrales FJ, Rubio A: Correlation between gene expression and GO semantic similarity. IEEEACM Trans Comput Biol Bioinforma 2005, 2:330-338.
  • [23]Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S: The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 2008, 83:610-615.
  • [24]Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S, Ott CE, Mundlos C, Horn D, Mundlos S, Robinson PN: Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet 2009, 85:457-464.
  • [25]Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010, 38(Web Server issue):W214-W220.
  • [26]Rappaport N, Nativ N, Stelzer G, Twik M, Guan-Golan Y, Iny Stein T, Bahir I, Belinky F, Morrey CP, Safran M, Lancet D: MalaCards: an integrated compendium for diseases and their annotation. Database (Oxford) 2013, 2013:bat018.
  • [27]Hoehndorf R, Schofield PN, Gkoutos GV: PhenomeNET: a whole-phenome approach to disease gene discovery. Nucleic Acids Res 2011, 39:e119.
  • [28]Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L: The human disease network. Proc Natl Acad Sci U S A 2007, 104:8685-8690.
  • [29]Adie EA, Adams RR, Evans KL, Porteous DJ, Pickard BS: SUSPECTS: enabling fast and effective prioritization of positional candidates. Bioinformatics 2006, 22:773-774.
  • [30]Wang J, Zhou X, Zhu J, Zhou C, Guo Z: Revealing and avoiding bias in semantic similarity scores for protein pairs. BMC Bioinformatics 2010, 11:290. BioMed Central Full Text
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