TY - JOUR T1 - Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits. JF - Genome Biol Y1 - 2013 A1 - Megraw, Molly A1 - Mukherjee, Sayan A1 - Ohler, Uwe KW - Algorithms KW - Animals KW - Arabidopsis KW - Computational Biology KW - Drosophila melanogaster KW - Gene Expression Regulation KW - Gene Regulatory Networks KW - Humans KW - MicroRNAs KW - Molecular Sequence Annotation KW - Nucleic Acid Conformation KW - Software KW - Transcription Factors AB -

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.

VL - 14 IS - 8 ER - TY - JOUR T1 - A transcription factor affinity-based code for mammalian transcription initiation. JF - Genome Res Y1 - 2009 A1 - Megraw, Molly A1 - Pereira, Fernando A1 - Jensen, Shane T A1 - Ohler, Uwe A1 - Hatzigeorgiou, Artemis G KW - Base Composition KW - Databases, Genetic KW - DNA KW - Gene Expression Regulation KW - Genome, Human KW - Humans KW - Promoter Regions, Genetic KW - RNA Polymerase II KW - TATA Box KW - Transcription Factors KW - Transcription Initiation Site KW - Transcription, Genetic AB -

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'-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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VL - 19 IS - 4 ER -