TY - JOUR T1 - TIPR: transcription initiation pattern recognition on a genome scale. JF - Bioinformatics Y1 - 2015 A1 - Morton, Taj A1 - Wong, Weng-Keen A1 - Megraw, Molly KW - Algorithms KW - Genomics KW - Machine Learning KW - Molecular Sequence Annotation KW - Sequence Analysis, DNA KW - Software KW - Transcription Initiation Site KW - Transcription Initiation, Genetic AB -

MOTIVATION: The computational identification of gene transcription start sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are arranged in different spatial patterns along the chromosome.

RESULTS: In this work, we present the Transcription Initiation Pattern Recognizer (TIPR), a sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize. TIPR predicts not only the locations of TSSs but also the expected spatial initiation pattern each TSS will form along the chromosome-a novel capability for TSS prediction algorithms. As spatial initiation patterns are associated with spatiotemporal expression patterns and gene function, this capability has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The high nucleotide resolution of this model locates TSSs within 10 nucleotides or less on average.

CONTACT: megrawm@science.oregonstate.edu.

[Software and Supplementary Materials Link]

VL - 31 IS - 23 ER - 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 -