diversity uses a Bayesian approach to detect multiple modes of protein-DNA binding from high throughput ChIP data. It is available both as a web based application and as a downloadable software.

diversity presents a Bayesian Model to identify different modes of protein-DNA bindings without relying on any motif database.

This is applied to ChIP-Seq datasets in the form of fasta file and it requires no prior knowledge regarding the binding characteristics of the protein or the motif databases. It reports the sequence regions that are likely to cause a specific region to be reported in the ChIP experiment.

The following files are saved upon execution in the OUTPUT directory:

modelWith_(num)_modes: This directory contains files having the details of each model.

logo_(num).png: It is the sequence logo corresponding to the motif learned for (num) mode.
info.txt: It contains motif positions, strand information for the respective modes.
pssm.txt: It contains PSSM for all the motifs for that mode.

all_models.html: Motifs corresponding to all the models that are learned can viewed at a glance.

settings.txt: A text file consisting of execution settings for diversity.

models.bin.p : A binary file which contains information regarding the models learned and the number of free parameters in every model. This is used in deciding the best model.

The web server provides an option for 2 types of input files. If input is a FASTA file, then the above files are created in the OUTPUT. If input is BED file, then along with the above files, following files are also created for each model learned.

alignedMotifs.png: All the sequences are aligned based on the mid point of the motif it contains.
conservation.png: This shows the conservation of the region which is 100bp upstream and 100bp downstream of the motif.
closestTSS.png: Box plot containing the distances from the closest TSS for each mode in the model.

Diversity can be run with several options in order to optimize the execution time and the results.

For a detailed documentation you may refer to Documentation.pdf.

Publication: Mitra, S., Biswas, A., & Narlikar, L. (2018). DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP. PLoS computational biology, 14(4), e1006090. [Full Text]

For more information, contact: l.narlikar@ncl.res.in.