@article {320, title = {Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits.}, journal = {Genome Biol}, volume = {14}, year = {2013}, month = {2013}, pages = {R85}, abstract = {

WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes.

}, keywords = {Algorithms, Animals, Arabidopsis, Computational Biology, Drosophila melanogaster, Gene Expression Regulation, Gene Regulatory Networks, Humans, MicroRNAs, Molecular Sequence Annotation, Nucleic Acid Conformation, Software, Transcription Factors}, issn = {1474-760X}, doi = {10.1186/gb-2013-14-8-r85}, author = {Megraw, Molly and Mukherjee, Sayan and Ohler, Uwe} } @article {326, title = {A transcription factor affinity-based code for mammalian transcription initiation.}, journal = {Genome Res}, volume = {19}, year = {2009}, month = {2009 Apr}, pages = {644-56}, abstract = {

The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45\%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5\&$\#$39;-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70\% of mammalian microRNAs based on currently available data.

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}, keywords = {Base Composition, Databases, Genetic, DNA, Gene Expression Regulation, Genome, Human, Humans, Promoter Regions, Genetic, RNA Polymerase II, TATA Box, Transcription Factors, Transcription Initiation Site, Transcription, Genetic}, issn = {1088-9051}, doi = {10.1101/gr.085449.108}, author = {Megraw, Molly and Pereira, Fernando and Jensen, Shane T and Ohler, Uwe and Hatzigeorgiou, Artemis G} }