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    Date Issued2014 (1)2013 (1)Author
    Fuxman Bass, Juan I. (2)
    Walhout, Albertha J. M. (2)Beittel, Nathan (1)Diallo, Alos (1)Mori, Akihiro (1)View MoreUMass Chan AffiliationProgram in Molecular Medicine (2)Program in Systems Biology (2)Document TypeJournal Article (2)KeywordComputational Biology (2)Systems Biology (2)Cell Biology (1)Chromatin (1)Epigenetics (1)View MoreJournalNature methods (1)Nucleic acids research (1)

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    Transcription factor binding to Caenorhabditis elegans first introns reveals lack of redundancy with gene promoters

    Fuxman Bass, Juan I.; Tamburino, Alex M.; Mori, Akihiro; Beittel, Nathan; Weirauch, Matthew T.; Reece-Hoyes, John S.; Walhout, Albertha J. M. (2014-01-07)
    Gene expression is controlled through the binding of transcription factors (TFs) to regulatory genomic regions. First introns are longer than other introns in multiple eukaryotic species and are under selective constraint. Here we explore the importance of first introns in TF binding in the nematode Caenorhabditis elegans by combining computational predictions and experimentally derived TF-DNA interaction data. We found that first introns of C. elegans genes, particularly those for families enriched in long first introns, are more conserved in length, have more conserved predicted TF interactions and are bound by more TFs than other introns. We detected a significant positive correlation between first intron size and the number of TF interactions obtained from chromatin immunoprecipitation assays or determined by yeast one-hybrid assays. TFs that bind first introns are largely different from those binding promoters, suggesting that the different interactions are complementary rather than redundant. By combining first intron and promoter interactions, we found that genes that share a large fraction of TF interactions are more likely to be co-expressed than when only TF interactions with promoters are considered. Altogether, our data suggest that C. elegans gene regulation may be additive through the combined effects of multiple regulatory regions.
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    Using networks to measure similarity between genes: association index selection

    Fuxman Bass, Juan I.; Diallo, Alos; Nelson, Justin; Soto, Juan M.; Myers, Chad L.; Walhout, Albertha J. M. (2013-11-26)
    Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.
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