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Identification of regulatory regions which confer muscle-specific gene expression (1998)
| Venue: | J. Mol. Biol |
| Citations: | 181 - 13 self |
BibTeX
@ARTICLE{Wasserman98identificationof,
author = {Wyeth W. Wasserman and James W. Fickett and Smithkline Beecham},
title = {Identification of regulatory regions which confer muscle-specific gene expression},
journal = {J. Mol. Biol},
year = {1998},
pages = {167--181}
}
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Abstract
For many newly sequenced genes, sequence analysis of the putative protein yields no clue on function. It would be bene®cial to be able to identify in the genome the regulatory regions that confer temporal and spatial expression patterns for the uncharacterized genes. Additionally, it would be advantageous to identify regulatory regions within genes of known expression pattern without performing the costly and time consuming laboratory studies now required. To achieve these goals, the wealth of case studies performed over the past 15 years will have to be collected into predictive models of expression. Extensive studies of genes expressed in skeletal muscle have identi®ed speci®c transcription factors which bind to regulatory elements to control gene expression. However, potential binding sites for these factors occur with suf®cient frequency that it is rare for a gene to be found without one. Analysis of experimentally determined muscle regulatory sequences indicates that muscle expression requires multiple elements in close proximity. A model is generated with predictive capability for identifying these muscle-speci®c regulatory modules. Phylogenetic footprinting, the identi®cation of sequences conserved between distantly related species, complements the statistical predictions. Through the use of logistic regression analysis, the model promises to be easily modi®ed to take advantage of the elucidation of additional factors, cooperation rules, and spacing constraints.
Keyphrases
regulatory region muscle-specific gene expression gene expression muscle regulatory sequence predictive capability potential binding site putative protein modi ed additional factor predictive model case study uncharacterized gene known expression pattern laboratory study extensive study spatial expression pattern muscle expression skeletal muscle phylogenetic footprinting identi cation muscle-speci regulatory module statistical prediction multiple element cooperation rule logistic regression analysis ed speci transcription factor regulatory element sequence analysis close proximity suf cient frequency
