title: Genome analysis with gene-indexing databases.
abstract: The recent release of the draft sequence and the eventual completion of the human genome present the scientific community with a rich source of data to mine. Yet, these data are content poor in the absence of additional correlative information. Expressed sequence tag (EST) datasets and their associated gene indices have existed for many years, and represent the first attempt at understanding the complexity of the genome. These datasets remain extremely important as information sources and, in particular, as tools for analyzing the completed genomes. Here, we discuss the nature of ESTs and their associated tools and gene-indexing databases. In particular, we will compare three EST gene indices (UNIGENE, Merck Gene Index Version 2.0 and Doubletwist CAT), discuss how these gene indices are applied for both genome analysis and drug discovery, and demonstrate their importance as a complementary dataset to the annotated human genome.
authors.affiliation: Department of Bioinformatics, Merck & Co., Inc., P.O. Box 2000-RY80-A1, Rahway, NJ 07065, USA. jeffrey_yuan@merck.com
authors.forename: J
authors.initials: J
authors.lastname: Yuan
authors.forename: Y
authors.initials: Y
authors.lastname: Liu
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: G
authors.initials: G
authors.lastname: Xie
authors.forename: R
authors.initials: R
authors.lastname: Blevins
mesh_terms: D016208:Databases, Factual; D015195:Drug Design; D020224:Expressed Sequence Tags; D016045:Human Genome Project; D006801:Humans; D016247:Information Storage and Retrieval; D020641:Polymorphism, Single Nucleotide