A Framework for Annotating Human Genome in Disease Context

Identification of gene-disease association is crucial to understanding disease mechanism. A rapid increase in biomedical literatures, led by advances of genome-scale technologies, poses challenge for manually-curated-based annotation databases to characterize gene-disease associations effectively and timely. We propose an automatic method-The Disease Ontology Annotation Framework (DOAF) to provide a comprehensive annotation of the human genome using the computable Disease Ontology (DO), the NCBO Annotator service and NCBI Gene Reference Into Function (GeneRIF). DOAF can keep the resulting knowledgebase current by periodically executing automatic pipeline to re-annotate the human genome using the latest DO and GeneRIF releases at any frequency such as daily or monthly. Further, DOAF provides a computable and programmable environment which enables large-scale and integrative analysis by working with external analytic software or online service platforms. A user-friendly web interface (doa.nubic.northwestern.edu) is implemented to allow users to efficiently query, download, and view disease annotations and the underlying evidences.

[1]  Itamar Simon,et al.  MILANO – custom annotation of microarray results using automatic literature searches , 2005, BMC Bioinformatics.

[2]  Betsy L. Humphreys,et al.  Technical Milestone: The Unified Medical Language System: An Informatics Research Collaboration , 1998, J. Am. Medical Informatics Assoc..

[3]  Patrick Ruch,et al.  Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction , 2008, BMC Bioinformatics.

[4]  Zhiyong Lu,et al.  PubMed and beyond: a survey of web tools for searching biomedical literature , 2011, Database J. Biol. Databases Curation.

[5]  Maricel G. Kann,et al.  Advances in translational bioinformatics: computational approaches for the hunting of disease genes , 2010, Briefings Bioinform..

[6]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[7]  Enrico W. Coiera,et al.  A PubMed-Wide Associational Study of Infectious Diseases , 2010, PloS one.

[8]  Kent A. Spackman,et al.  SNOMED RT: a reference terminology for health care , 1997, AMIA.

[9]  Sam Zaremba,et al.  Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens , 2009, BMC Bioinformatics.

[10]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2002, Nucleic Acids Res..

[11]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[12]  M. Duffy,et al.  ADAM-17 Expression in Breast Cancer Correlates with Variables of Tumor Progression , 2007, Clinical Cancer Research.

[13]  Cory Y. McLean,et al.  GREAT improves functional interpretation of cis-regulatory regions , 2010, Nature Biotechnology.

[14]  Hagit Shatkay,et al.  Mining the Biomedical Literature in the Genomic Era: An Overview , 2003, J. Comput. Biol..

[15]  W. Kibbe,et al.  Annotating the human genome with Disease Ontology , 2009, BMC Genomics.

[16]  N. Campbell.  Genetic association database , 2004, Nature Reviews Genetics.

[17]  F. Dhombres,et al.  Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users , 2012, Human mutation.

[18]  Hans-Peter Kriegel,et al.  Extraction of semantic biomedical relations from text using conditional random fields , 2008, BMC Bioinformatics.

[19]  Zhiyong Lu,et al.  Generif Quality Assurance as Summary Revision , 2006, Pacific Symposium on Biocomputing.

[20]  Mark A. Musen,et al.  The Open Biomedical Annotator , 2009, Summit on translational bioinformatics.

[21]  Lada A. Adamic,et al.  A literature based method for identifying gene-disease connections , 2002, Proceedings. IEEE Computer Society Bioinformatics Conference.