The Merck Gene Index Browser: an Extensible Data Integration System for Gene Finding, Gene Characterization and EST Data Mining

Barbara A. Eckman 2, Jeffrey S. Aaronson 2, Joseph A. Borkowski, Wendy J. Bailey, Keith O. Elliston 3, Alan R. Williamson 1, Richard A. Blevins

Department of Bioinformatics, Merck Research Laboratories, West Point, PA and Rahway, NJ, USA 1 Department of Immunology, Merck Research Laboratories, Rahway, NJ, USA

Bioinformatics, 14(1), 2-13 (Jan 1998)

Abstract

Motivation: To make effective use of the vast amounts of expressed sequence tag (EST) sequence data generated by the Merck-sponsored EST project and other similar efforts, sequences must be organized into gene classes, and scientists must be able to `mine' the gene class data in the context of related genomic data.

Results: This paper presents the Merck Gene Index browser, an easily extensible, World Wide Web-based system for mining the Merck Gene Index (MGI) and related genomic data. The MGI is a non-redundant set of clones and sequences, each representing a distinct gene, constructed from all high-quality 3[prime] EST sequences generated by the Merck-sponsored EST project. The MGI browser integrates data from a variety of sources and storage formats, both local and remote, using an eclectic integration strategy, including a federation of relational databases, a local data warehouse and simple hypertext links. Data currently integrated include: LENS cDNA clone and EST data, dbEST protein and non-EST nucleic acid similarity data, WashU sequence chromatograms, Entrez sequence and Medline entries, and UniGene gene clusters. Flatfile sequence data are accessed using the Bioapps server, an internally developed client-server system that supports generic sequence analysis applications. Browser data are retrieved and formatted by means of the Bioinformatics Data Integration Toolkit (B-DIT), a new suite of Perl routines.

Availability: Software is available on request from the authors.

Contact: barbara_eckman@sbphrd.com