CSI-OMIM - Clinical Synopsis Search in OMIM

Abstract

BackgroundThe OMIM database is a tool used daily by geneticists. Syndrome pages include a Clinical Synopsis section containing a list of known phenotypes comprising a clinical syndrome. The phenotypes are in free text and different phrases are often used to describe the same phenotype, the differences originating in spelling variations or typing errors, varying sentence structures and terminological variants.These variations hinder searching for syndromes or using the large amount of phenotypic information for research purposes. In addition, negation forms also create false positives when searching the textual description of phenotypes and induce noise in text mining applications.DescriptionOur method allows efficient and complete search of OMIM phenotypes as well as improved data-mining of the OMIM phenome. Applying natural language processing, each phrase is tagged with additional semantic information using UMLS and MESH. Using a grammar based method, annotated phrases are clustered into groups denoting similar phenotypes. These groups of synonymous expressions enable precise search, as query terms can be matched with the many variations that appear in OMIM, while avoiding over-matching expressions that include the query term in a negative context. On the basis of these clusters, we computed pair-wise similarity among syndromes in OMIM. Using this new similarity measure, we identified 79,770 new connections between syndromes, an average of 16 new connections per syndrome. Our project is Web-based and available at http://fohs.bgu.ac.il/s2g/csiomimConclusionsThe resulting enhanced search functionality provides clinicians with an efficient tool for diagnosis. This search application is also used for finding similar syndromes for the candidate gene prioritization tool S2G.The enhanced OMIM database we produced can be further used for bioinformatics purposes such as linking phenotypes and genes based on syndrome similarities and the known genes in Morbidmap.

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“…Maximal twopoint LOD score (SUPERLINK) 27 was 4.1 (theta ¼ 0) at D12S1580. Of the 40 genes within that locus, our S2G software 28,29 identified GUCY2C (MIM 601330) as the primary candidate gene. Sequencing of the entire coding sequence and exon-intron boundaries of GUCY2C (NM_004963.3) identified a single homozygous mutation, c.1160A>G, leading to p.(Asp387Gly) amino acid substitution in the encoded protein.…”

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“…Perhaps the most popular disease-oriented database is McKusick's Online Mendelian Inheritance in Man (OMIM) database [25,26], which is a manually curated database of human genes and genetic disorders including genetic phenotypes [26]. Since its establishment in early 1960s, many researchers have contributed to various aspects of the OMIM database such as developing extra features resulting in OMIM derivatives as PhenOMIM (for phenotypic comparison) [27], OMiR (to reveal associations between OMIM diseases and microR-NAs) [28], CSI-OMIM (assisting clinical synopsis search in OMIM) [29], CGMIM (for text-mining of cancer genes) [30], and many other applications. On the other end of the spectrum is the dbSNP [31,32] database at National Center for Biotechnology Information (NCBI).…”

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“…They developed a web service called GFINDer to analyze phenotypes of inherited disorders (Masseroli et al 2005 b). Cohen et al also developed a web service to search the OMIM CS section called CSI-OMIM (Cohen et al 2011). Using CS terms, researchers can retrieve disease information from OMIM without using text-mining techniques.…”