THiCweed: fast, sensitive motif finding by clustering big data sets

Agrawal, A.; Narlikar, L.; Siddharthan, R.

doi:10.1101/104109

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bioRxiv · 10.1101/104109

THiCweed: fast, sensitive motif finding by clustering big data sets

Agrawal, A.·Narlikar, L.·Siddharthan, R.

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Abstract

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 towards 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 hours, 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.\n\nTHiCweed performs best with large \"window\" sizes ([≥] 50bp), much longer than typical binding sites (7-15 base pairs). 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 < 5% of the input, all of which appear biologically relevant. We also find recurring sequence patterns across diverse ChIP-seq data sets, possibly related to chromatin architecture and looping. THiCweed thus goes beyond traditional motif-finding to give new insights into genomic TF binding complexity.

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Agrawal, A., Narlikar, L., Siddharthan, R.. 2017-01-29. THiCweed: fast, sensitive motif finding by clustering big data sets. https://doi.org/10.1101/104109

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