MatrixREDUCE source code is freely available for non-commercial use at http://www.bussemakerlab.org/. The software runs on Linux, Unix, and Mac OS X.

The steady-state abundance of an mRNA is determined by the balance between transcription and decay. Although regulation of transcription has been well studied both experimentally and computationally, regulation of transcript stability has received little attention. We developed an algorithm, MatrixREDUCE, that discovers the position-specific affinity matrices for unknown RNAbinding factors and infers their condition-specific activities, using only genomic sequence data and steady-state mRNA expression data as input. We identified and computationally characterized the binding sites for six mRNA stability regulators in Saccharomyces cerevisiae, which include two members of the Pumilio-homology domain (Puf) family of RNA-binding proteins, Puf3p and Puf4p. We provide computational and experimental evidence that regulation of mRNA stability by these factors is modulated in response to a variety of environmental stimuli.cis-regulatory element ͉ gene expression ͉ microarray ͉ mRNA decay ͉ Puf protein

The extent of gene regulation in cell differentiation is poorly understood. We previously used saturation mutagenesis to identify 18 genes that are needed for the development and function of a single type of sensory neuron--the touch receptor neuron for gentle touch in Caenorhabditis elegans. One of these genes, mec-3, encodes a transcription factor that controls touch receptor differentiation. By culturing and isolating wild-type and mec-3 mutant cells from embryos and applying their amplified RNA to DNA microarrays, here we have identified genes that are known to be expressed in touch receptors, a previously uncloned gene (mec-17) that is needed for maintaining touch receptor differentiation, and more than 50 previously unknown mec-3-dependent genes. These genes are randomly distributed in the genome and under-represented both for genes that are co-expressed in operons and for multiple members of gene families. Using regions 5' of the start codon of the first 20 genes, we have also identified an over-represented heptanucleotide, AATGCAT, that is needed for the expression of touch receptor genes.

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. Here we describe T-profiler, a tool that uses the t-test to score changes in the average activity of predefined groups of genes.

BackgroundFunctional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data.ResultsWe present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative.ConclusionOur reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at .

Various algorithms are available for predicting mRNA expression and modeling gene regulatory processes. They differ in whether they rely on the existence of modules of coregulated genes or build a model that applies to all genes, whether they represent regulatory activities as hidden variables or as mRNA levels, and whether they implicitly or explicitly model the complex cis-regulatory logic of multiple interacting transcription factors binding the same DNA. The fact that functional genomics data of different types reflect the same molecular processes provides a natural strategy for integrative computational analysis. One promising avenue toward an accurate and comprehensive model of gene regulation combines biophysical modeling of the interactions among proteins, DNA, and RNA with the use of large-scale functional genomics data to estimate regulatory network connectivity and activity parameters. As the ability of these models to represent complex cis-regulatory logic increases, the need for approaches based on cross-species conservation may diminish.

Accurate and comprehensive information about the nucleotide sequence specificity of trans-acting factors (TFs) is essential for computational and experimental analyses of gene regulatory networks. We present the Yeast Transfactome Database, a repository of sequence specificity models and condition-specific regulatory activities for a large number of DNA- and RNA-binding proteins in Saccharomyces cerevisiae. The sequence specificities in TransfactomeDB, represented as position-specific affinity matrices (PSAMs), are directly estimated from genomewide measurements of TF-binding using our previously published MatrixREDUCE algorithm, which is based on a biophysical model. For each mRNA expression profile in the NCBI Gene Expression Omnibus, we used sequence-based regression analysis to estimate the post-translational regulatory activity of each TF for which a PSAM is available. The trans-factor activity profiles across multiple experiments available in TransfactomeDB allow the user to explore potential regulatory roles of hundreds of TFs in any of thousands of microarray experiments. Our resource is freely available at http://bussemakerlab.org/TransfactomeDB/

Gene expression is regulated at each step from chromatin remodeling through translation and degradation. Several known RNA-binding regulatory proteins interact with specific RNA secondary structures in addition to specific nucleotides. To provide a more comprehensive understanding of the regulation of gene expression, we developed an integrative computational approach that leverages functional genomics data and nucleotide sequences to discover RNA secondary structuredefined cis-regulatory elements (SCREs). We applied our structural cis-regulatory element detector (StructRED) to microarray and mRNA sequence data from Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We recovered the known specificities of Vts1p in yeast and Smaug in flies. In addition, we discovered six putative SCREs in flies and three in humans. We characterized the SCREs based on their condition-specific regulatory influences, the annotation of the transcripts that contain them, and their locations within transcripts. Overall, we show that modeling functional genomics data in terms of combined RNA structure and sequence motifs is an effective method for discovering the specificities and regulatory roles of RNA-binding proteins.