Narlikar, L. | bioRxiv Science

bioRxiv2017

DIVERSITY In Binding, Regulation, And Evolution Revealed From High-Throughput ChIP

A high-throughput chromatin immunoprecipitation (ChIP) experiment is like a black-box: it reports all regions that are associated with the profiled protein based on the initial cross-linking step. These regions can be a highly diverse set of DNA sequences, with some making direct contact with the protein, some binding through intermediaries, and some being a result of long-range interactions involving the protein. We present O_SCPLOWDIVERSITYC_SCPLOW, a method that identifies the distinct components of such a mixture, leaving no data behind, while at the same time, using no prior motif knowledge. Using the example of the REST protein, we show that these different components give insights into the various complexes that may be forming along the chromatin and their regulatory functions.\n\nhttp://diversity.ncl.res.in/ (webserver)\n\nhttps://github.com/NarlikarLab/DIVERSITY (standalone for Mac OSX/Linux)

bioinformatics↗

bioRxiv2017

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

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.

bioinformatics↗