PCAN: phenotype consensus analysis to support disease-gene association

BMC Bioinformatics

Software

Patrice Godard¹  Matthew Page² 

[1] Clarivate Analytics (formerly the IP & Science business of Thomson Reuters), 5901 Priestly Dr., #200, 92008, Carlsbad, CA, USA;Translational Bioinformatics, UCB Pharma, 208 Bath Road, SL1 3WE, Slough, UK;

关键词: Disease-gene association;    Phenotype;    Semantic similarity;    Biological networks;    Genetics;   

DOI  :  10.1186/s12859-016-1401-2

 received in 2016-05-23, accepted in 2016-12-01,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundBridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest.ResultsWe describe Phenotype Consensus Analysis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene’s signaling neighborhood. We demonstrate that significant phenotype consensus (p < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high quality String interactions + Metabase pathways and use Joubert Syndrome to demonstrate the ease with which a significant result can be interrogated to highlight discriminatory traits linked to mechanistically related genes.ConclusionsWe advocate phenotype consensus as an intuitive and versatile method to aid disease-gene association, which naturally lends itself to the mechanistic deconvolution of diverse phenotypes. We provide PCAN to the community as an R package (http://bioconductor.org/packages/PCAN/) to allow flexible configuration, extension and standalone use or integration to supplement existing gene prioritization workflows.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

Fig. 3