Identification of "pathologs" (disease-related genes) from the RIKEN mouse cDNA dataset using human curation plus FACTS, a new biological information extraction system

BACKGROUND: A major goal in the post-genomic era is to identify and characterise disease susceptibility genes and to apply this knowledge to disease prevention and treatment. Rodents and humans have remarkably similar genomes and share closely related biochemical, physiological and pathological pathways. In this work we utilised the latest information on the mouse transcriptome as revealed by the RIKEN FANTOM2 project to identify novel human disease-related candidate genes. We define a new term "patholog" to mean a homolog of a human disease-related gene encoding a product (transcript, anti-sense or protein) potentially relevant to disease. Rather than just focus on Mendelian inheritance, we applied the analysis to all potential pathologs regardless of their inheritance pattern. RESULTS: Bioinformatic analysis and human curation of 60,770 RIKEN full-length mouse cDNA clones produced 2,578 sequences that showed similarity (70–85% identity) to known human-disease genes. Using a newly developed biological information extraction and annotation tool (FACTS) in parallel with human expert analysis of 17,051 MEDLINE scientific abstracts we identified 182 novel potential pathologs. Of these, 36 were identified by computational tools only, 49 by human expert analysis only and 97 by both methods. These pathologs were related to neoplastic (53%), hereditary (24%), immunological (5%), cardio-vascular (4%), or other (14%), disorders. CONCLUSIONS: Large scale genome projects continue to produce a vast amount of data with potential application to the study of human disease. For this potential to be realised we need intelligent strategies for data categorisation and the ability to link sequence data with relevant literature. This paper demonstrates the power of combining human expert annotation with FACTS, a newly developed bioinformatics tool, to identify novel pathologs from within large-scale mouse transcript datasets

IMMUNOINFORMATICS

vii, 200 hlm ; 15,5 x 23,5 c

IMMUNOINFORMATICS

xvii, 186 hlm ; 15 x 23,5 c

Immunoinformatics

This peer-reviewed book series offers insight on immunology for 21st century. The technological revolution has borne advances in high-throughput instrumentation and information technology, initiating a renaissance for biomathematics, and biostatistics. Cross-fertilization between genomics and immunology has led to a new field called immunomics, transforming the way in which theoretical, clinical and applied immunology are practiced. Immunomics Reviews will cover integrative approaches and applications to the theory and practice of immunology and explore synergistic effects resulting from a combination of technological advances and the latest analytical tools with the traditional fields of basic and clinical immunology. Volume 1: Immunoinformatics Edited by Christian Schnbach Immunoinformatics is an emerging subdiscipline of bioinformatics. It utilizes mathematics, information science, computer engineering, genomics, proteomics and immunological methods to bridge immunology and informatics. Similar to bioionformatics which became a driving force in genome research, immunoinformatics enables data-driven research strategies and systems approaches that aim at understanding the networks regulating the immune system. Considering the breath of topic, Immunoinformatics was composed to provide a cross-section of research ranging from data integration, epitope predictions to systems level applications. In ten chapters experts in the field introduce and discuss research strategies for immunologists and bioinformaticians who wish to endeavour existing and new approaches to gain insight into the workings of the immune system.200 page(s

An update on the functional molecular immunology (FIMM) database

10.2165/00822942-200504010-00003Applied Bioinformatics4125-31ABPI