@article {44601,
	title = {THiCweed: fast, sensitive detection of sequence features by clustering big datasets},
	journal = {Nucleic Acids Research},
	volume = {46},
	year = {2018},
	month = {MAR},
	pages = {e29},
	abstract = {<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 {\textquoteleft}window{\textquoteright} 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>},
	issn = {0305-1048},
	doi = {10.1093/narlgkx1251},
	author = {Agrawal, Ankit and Sambare, Snehal V. and Narlikar, Leelavati and Siddharthan, Rahul}
}
