01215nas a2200157   4500008004100000022001400041245011600055210006900171260007200240300001200312490000700324520056600331100001700897700002400914856011900938       2016                        eng d  a1367-480300aNo promoter left behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites0 aNo promoter left behind NPLB learn de novo promoter architecture  aGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLANDbOXFORD UNIV PRESScMAR  a779-7810 v323 a<p>Promoters have diverse regulatory architectures and thus activate genes differently. For example, some have a TATA-box, many others do not. Even the ones with it can differ in its position relative to the transcription start site (TSS). No Promoter Left Behind (NPLB) is an efficient, organism-independent method for characterizing such diverse architectures directly from experimentally identified genome-wide TSSs, without relying on known promoter elements. As a test case, we show its application in identifying novel architectures in the fly genome.</p>1 aMitra, Sneha1 aNarlikar, Leelavati  uhttps://library.ncl.res.in/content/no-promoter-left-behind-nplb-learn-de-novo-promoter-architectures-genome-wide-002620nas a2200157   4500008004100000245008700041210006900128260000800197300002900205490000700234520204800241100001702289700002002306700002402326856011202350       2018                        eng d00aDiversity in binding, regulation, and evolution revealed from high-throughput ChIP0 aDiversity in binding regulation and evolution revealed from high  cAPR  aArticle Number: e10060900 v143 a<p>Genome-wide in vivo protein-DNA interactions are routinely mapped using high-throughput chromatin immunoprecipitation (ChIP). ChIP-reported regions are typically investigated for enriched sequence-motifs, which are likely to model the DNA-binding specificity of the profiled protein and/or of co-occurring proteins. However, simple enrichment analyses can miss insights into the binding-activity of the protein. Note that ChIP reports regions making direct contact with the protein as well as those binding through intermediaries. For example, consider a ChIP experiment targeting protein X, which binds DNA at its cognate sites, but simultaneously interacts with four other proteins. Each of these proteins also binds to its own specific cognate sites along distant parts of the genome, a scenario consistent with the current view of transcriptional hubs and chromatin loops. Since ChIP will pull down all X-associated regions, the final reported data will be a union of five distinct sets of regions, each containing binding sites of one of the five proteins, respectively. Characterizing all five different motifs and the corresponding sets is important to interpret the ChIP experiment and ultimately, the role of X in regulation. We present DIVERSITY which attempts exactly this: it partitions the data so that each partition can be characterized with its own de novo motif. DIVERSITY uses a Bayesian approach to identify the optimal number of motifs and the associated partitions, which together explain the entire dataset. This is in contrast to standard motif finders, which report motifs individually enriched in the data, but do not necessarily explain all reported regions. We show that the different motifs and associated regions identified by DIVERSITY give insights into the various complexes that may be forming along the chromatin, something that has so far not been attempted from ChIP data. Webserver at nci.res.i, if; standalone (Mac OS X/Linux) from from https://github.com/NarlikarLab/DIVERSITY/releases/tag/v1.0.0.</p>1 aMitra, Sneha1 aBiswas, Anushua1 aNarlikar, Leelavati  uhttps://library.ncl.res.in/content/diversity-binding-regulation-and-evolution-revealed-high-throughput-chip