Predicting gene expression from sequence.
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.
Beer
Michael A
MA
Lewis-Sigler Institute for Integrative Genomics and Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
Tavazoie
Saeed
S
D000818:Animals; D001483:Base Sequence / Q000235:genetics*; D001499:Bayes Theorem; D017173:Caenorhabditis elegans; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics*; D005796:Genes / Q000235:genetics*; D016678:Genome; D015233:Models, Statistical; D005810:Multigene Family / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D011237:Predictive Value of Tests; D011995:Recombination, Genetic / Q000235:genetics; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae; D014157:Transcription Factors / Q000235:genetics
D014157:Transcription Factors