Predicting gene expression from sequence. Export

@article{citeulike:576440, abstract = {We describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73\% of genes in Saccharomyces cerevisiae, utilizing microarray expression data and sequences in the 800 bp upstream of genes. Application to Caenorhabditis elegans identifies predictive regulatory elements and combinatorial rules that control the phased temporal expression of transcription factors, histones, and germline specific genes. Successful prediction requires diverse and complex rules utilizing AND, OR, and NOT logic, with significant constraints on motif strength, orientation, and relative position. This system generates a large number of mechanistic hypotheses for focused experimental validation, and establishes a predictive dynamical framework for understanding cellular behavior from genomic sequence.}, address = {Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.}, author = {Beer, M. A. and Tavazoie, S.}, citeulike-article-id = {576440}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15084257}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15084257}, citeulike-linkout-2 = {http://www.wormbase.org/db/misc/paper?name=WBPaper00006508}, day = {16}, issn = {0092-8674}, journal = {Cell}, keywords = {bayesian, expression, predictions, regulation, tfbs}, month = {April}, number = {2}, pages = {185--198}, posted-at = {2006-06-30 12:16:01}, priority = {2}, title = {Predicting gene expression from sequence.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15084257}, volume = {117}, year = {2004} }

TY - JOUR ID - citeulike:576440 L3 - citeulike-article-id:576440 N2 - We describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73% of genes in Saccharomyces cerevisiae, utilizing microarray expression data and sequences in the 800 bp upstream of genes. Application to Caenorhabditis elegans identifies predictive regulatory elements and combinatorial rules that control the phased temporal expression of transcription factors, histones, and germline specific genes. Successful prediction requires diverse and complex rules utilizing AND, OR, and NOT logic, with significant constraints on motif strength, orientation, and relative position. This system generates a large number of mechanistic hypotheses for focused experimental validation, and establishes a predictive dynamical framework for understanding cellular behavior from genomic sequence. IS - 2 JF - Cell SN - 0092-8674 AD - Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA. EP - 198 TI - Predicting gene expression from sequence. VL - 117 SP - 185 KW - bayesian KW - expression KW - predictions KW - regulation KW - tfbs AU - Beer, MA AU - Tavazoie, S PY - 2004/04/16/ UR - http://view.ncbi.nlm.nih.gov/pubmed/15084257 ER -