02022nas a2200181   4500008004100000022001400041245008800055210006900143260000800212300000800220490000700228520140000235100001901635700002401654700002401678700002301702856011501725       2018                        eng d  a0305-104800aTHiCweed: fast, sensitive detection of sequence features by clustering big datasets0 aTHiCweed fast sensitive detection of sequence features by cluste  cMAR  ae290 v463 a<p>We present THiCweed, a new approach to analyzing transcription factor binding data from high-throughput chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. THiCweed clusters bound regions based on sequence similarity using a divisive hierarchical clustering approach based on sequence similarity within sliding windows, while exploring both strands. ThiCweed is specially geared toward data containing mixtures of motifs, which present a challenge to traditional motif-finders. Our implementation is significantly faster than standard motif-finding programs, able to process 30 000 peaks in 1-2 h, on a single CPU core of a desktop computer. On synthetic data containing mixtures of motifs it is as accurate or more accurate than all other tested programs. THiCweed performs best with large `window' sizes (&gt;50 bp), much longer than typical binding sites (7-15 bp). On real data it successfully recovers literature motifs, but also uncovers complex sequence characteristics in flanking DNA, variant motifs and secondary motifs even when they occur in &lt;5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq datasets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif finding to give new insights into genomic transcription factor-binding complexity.</p>1 aAgrawal, Ankit1 aSambare, Snehal, V.1 aNarlikar, Leelavati1 aSiddharthan, Rahul  uhttps://library.ncl.res.in/content/thicweed-fast-sensitive-detection-sequence-features-clustering-big-datasets