We present diversity which attempts exactly this: it partitions the data so that each partition can be characterized with its own de novo motif. Diversity uses a Bayesian approach to identify the optimal number of motifs and the associated partitions, which together explain the entire dataset. This is in contrast to standard motif finders, which report motifs individually enriched in the data, but do not necessarily explain all reported regions. We show that the different motifs and associated regions identified by diversity give insights into the various complexes that may be forming along the chromatin, something that has so far not been attempted from ChIP data. Webserver at http://diversity.ncl.res.in/; standalone (Mac OS X/Linux) from https://github.com/NarlikarLab/DIVERSITY/releases/tag/v1.0.0.Genome-wide in vivo protein-DNA interactions are routinely mapped using high-throughput chromatin immunoprecipitation (ChIP). ChIP-reported regions are typically investigated for enriched sequence-motifs, which are likely to model the DNA-binding specificity of the profiled protein and/or of co-occurring proteins. However, simple enrichment analyses can miss insights into the binding-activity of the protein. Note that ChIP reports regions making direct contact with the protein as well as those binding through intermediaries. For example, consider a ChIP experiment targeting protein X, which binds DNA at its cognate sites, but simultaneously interacts with four other proteins. Each of these proteins also binds to its own specific cognate sites along distant parts of the genome, a scenario consistent with the current view of transcriptional hubs and chromatin loops. Since ChIP will pull down all X-associated regions, the final reported data will be a union of five distinct sets of regions, each containing binding sites of one of the five proteins, respectively. Characterizing all five different motifs  iversity models the fact that since a ChIP experiment pulls down regions participating in all complexes involving the profiled protein, the reported regions are in all likelihood, a collection of different types of protein-DNA contacts. Diversity asks a different question: what sequence component caused a specific region to be reported in a ChIP experiment? The answer, in combination with additional data such as sequence conservation, SNPs, chromatin structure, downstream gene-expression, etc. can yield insights into the diverse regulatory mechanisms at play. The added benefits of a webserver and a standalone parallel version make diversity a practical tool for discovering new biology from ChIP experiments.A high-throughput chromatin immunoprecipitation (ChIP) experiment identifies genomic regions bound by a protein in vivo. Current motif-discovery approaches seek an enriched motif signature in the reported regions, which they can attribute to the protein’s binding preferences. However, D PLoS Computational Biology Software paper.This is a Transcriptional regulation is a complex cellular process, governed in large part by interactions between chromatin remodeling complexes, transcription factors (TFs), and specific sequences on the DNA. The importance of these sequences, also known as regulatory regions, has been well-documented in various biological processes such as development, differentiation, maintenance, and apoptosis , 2. TherHowever, in spite of these efforts, we still do not know how regulatory information is encoded in the four-letter “alphabet” of our genome . We attrdiversity, a method that appreciates the fact that since a ChIP experiment pulls down regions participating in all complexes involving the profiled protein ∝em∣Mm)dθmwThe prioθm (from ).m as well as the vector of values for the widths w1, w2, …, wm. As a result, exponentially many more models had to be learned by iterating over all “reasonable” values of w. This not only limited the method to smaller datasets, but could never identify motifs with arbitrary lengths. Diversity models the widths as parameters of the model instead of as the structure of the model. The widths are sampled along with the other parameters. For each mode k, wk is sampled from a pool of a few values: it is allowed to not change, or increase/decrease by one position on the left and right of the motif.In our original method, a model was defined with n iterations and stop sampling if the slope is close to zero. We also have a check in place for the number of Zi & Ii sampling iterations exceeding 2n2, to ensure the program does report a model in reasonable time in case of a particularly unlucky initialisation. But the user has an option to increase (or decrease) this maximum iterations limit, if time is to be traded for more accurate results (or vice versa). At the end, we use a hill climbing approach starting from the sample with highest posterior probability.Detecting convergence of a sampler is non trivial, especially when the target distribution is irregular. But we do not really need samples from the posterior, we only want the MAP estimate. We fit a linear curve through the last iversity takes as input a fasta file corresponding to ChIP-bound regions. The webserver also allows the input to be a bed file, in which case the reference genome has to be selected from a drop-down menu. Diversity can be run with several additional options such as changing the range of the motif width, number of modes, and many more. See Documentation  and position of the site (Zi), as well as a text file with all the mode parameters (ϕ) and corresponding sequence logos. It also calculates and reports the best model amongst all.DIn the webserver, if the input is in the form of a bed file, then along with the above output, following additional images are created: aligned motifs based on their midpoint per sequence, phastCons scores at the sequences, and boxplots of distances between the modes and the closest transcription start site (TSS), similar to iversity was run with default parameters except for an increased number of sampling start points (10 instead of the default five) and models with modes in the range of 1 to 20. PhastCons scores were used to assess sequence conservation and refGene.txt for gene analysis from UCSC genome browser. The ChIP signal for each region as reported by the respective studies was considered the ChIP score for that sequence when constructing plots. JASPAR [diversity to create logos of the PWMs. All p-value based comparisons across sets are done using the Wilcoxon test. Violin plots were constructed using the vioplot package of R.All data are publicly available, accession numbers from GEO are mentioned in parentheses. Fly CTCF ChIP and RNA-seq data (GSE24449) is from Negre et al. . Su(Hw) . JASPAR  motifs a. JASPAR  was used. JASPAR . Weblogo. JASPAR  has been-nmotifs 20 -minw 6 -revcomp -p 16.  and once when it was fixed to 20, only to compare with InMoDe.  is not.Modes 5, 6, 7, and 8 have a lower ChIP score than the first four modes and have no resemblance to any CTCF literature motif, suggesting these may not be direct binding sites. Instead, mode 5 matches the motif of a newly discovered insulator protein Pita , mode 6 ur flies . This suThe first three CTCF modes are far more variable in terms of where they bind along the genome with respect to gene , which ip-value< 10−15), suggesting that CTCF possibly activates transcription of these genes, by indirectly binding to the transcription initiation machinery assembling at these promoters. This could admittedly be a case of highly expressed promoters getting reported in the ChIP experiment, which are not specific to the profiled TF (Discussion) [Mode 7 is a well-established core-promoter motif, with almost half of the instances occurring within 100bp of a TSS in the reported relative orientation . Furthercussion) . But evecussion) .diversity on the Su(Hw) set (We next explored ChIP datasets of the co-factors of CTCF identified from (Hw) set . This sudiversity discovers six variants of the known Su(Hw) motif  motif , of whicw) motif . We can iversity finds the literature Pita motif (mode 1), but also an additional variant, with the central piece differing in a fifth of sequences  motifs . Taken together, this means, a CTCF ChIP pulls down direct sites of Pita and Su(Hw), a Pita ChIP pulls down direct sites of CTCF and Su(Hw), but Su(Hw) does not pull either sites of the other two  has been shown to repress neuronal genes in non-neuronal cell-types and play regulatory roles in differentiation and development of neuronal cells . It bindz et al.  have comdiversity also finds only a small fraction of sequences (≈3.5%) contributing to a mode that looks like RE-1 . YY1 plays a key role in neuronal development and has been shown to positively regulate REST itself [p < 10−5) to the TSS.DT itself . It is vT itself . Both moiversity results suggest it also interacts with REST.Mode 9, which matches the winged helix RFX family, is the only other significantly conserved mode. All proteins in this family recognize a near-identical palindromic motif . While tMode 5 is a near perfect match to the SoxB1 proteins: Sox2 and Sox3, which have been shown be critical for neuronal differentiation . Mode 10Modes differ in terms of distance from the closest genes and expression of the downstream gene. MEIS1, RFX2, and mode 4 are far more variable suggesting they might be distal regulatory regions such as enhancers or silencers. Without additional information such as ChIP of these proteins in the same set or chromatin structure information, we cannot say much more about what complexes must be forming along the genome. However these results, from a single ChIP experiment, do point towards a rather complex TF-TF interaction landscape for REST in neuronal cells.diversity finds only variants of the canonical 21bp RE-1 motif, which contains two informative pieces separated by a small gap. These asymmetric half-sites of RE-1 have been shown to occur individually in REST-bound regions of non-neuronal cells [p < 10−5).In non-neuronal cell-types the picture is dramatically different from neuronal cells. In GM12878 cell line , diversial cells : these aThe full sites are more conserved than half-sites. Chromatin structure around full sites is strikingly different: nucleosome are strongly phased and evenly spaced around the full sites similar to what is known to happen with the insulator binding protein CTCF in mammals . A weak diversity to a collection of diverse TFs in the K562 cell-line . RUNX1, also a cell-type specific protein, has a mode that resembles the RUNX1 motif, one that is a variant and a third that matches SP1, a known RUNX1 co-factor in leukemia [Here we apply leukemia . In all TGAsTCA, there are no common modes between the two TF datasets. In fact, in the case of FOSL1, the mode with the highest ChIP score is a strong motif but not recorded in the standard databases.FOSL1 from the Fos family and JUNB of the Jun family are part of the AP-1 complex, which is involved in multiple cellular processes. AP-1 complexes are known to be instrumental in looping DNA and involved in enhancer-promoter interactions , which eAANNGAAA. Variants of this motif are discovered as distinct modes by diversity. IRFs have different C-terminal regions which help facilitate specific protein-protein interactions [IRF2 is one of the interferon regulatory factors (IRFs), which bind to ractions . This madiversity detects as separate modes.THAP1 is a zinc finger protein that is known to interact majorly with a general transcriptional regulator HCFC1. HCFC1 does not bind DNA directly but via interactions with other TFs such as YY1, E2F1, and THAP11 , all of diversity, whose goal is to identify a set of motifs, which together explain the entire dataset. Here we compare and contrast diversity’s output with that from two standard approaches: MEME, which is targeted for wide motifs corresponding to complexes, and DREME, which is targeted for finding shorter monomeric motifs likely to be cofactors [more overrepresented than the individual variants, it gets detected first and the half-sites subsequently get detected if they are individually overrepresented in the full dataset. We see a similar picture with DREME: it never identifies the full site, possibly because of its bias to short motifs.The goal in traditional motif discovery is to find a statistically overrepresented motif, typically one that appears in a large fraction of the data . To idenofactors . We disciversity, in its Bayesian formulation, uses one primary hyperparameter λ to determine how much to penalize models with more modes. The number of motifs returned by each of the three programs is different, as well. In GM12878, for example, although neither MEME nor DREME finds the full RE-1 motif, DREME finds eight non-RE-1 motifs and MEME finds 17, some of which are supported by only four sites. Indeed, multiple user-defined/default parameter values such as the minimum number of sites for a motif, the E-value cut-off for enrichment, etc. decide whether a motif will be reported in these methods. Ddiversity finds as optimal. We set the width to 20 , before it can report the optimal model, the total time taken for the neuronal set is about 27 hours. On the same machine, MEME, also running in parallel mode, takes over 40 hours to find 20 motifs  of downstream genes, etc. In particular, we showed that diversity can give new insights into protein-DNA interactions even in widely studied TFs like the fly CTCF: it appears to bind human-like CTCF sites with lower efficiency; it interacts with specific promoter architectures; and that CTCF-Pita is likely a melanogaster subgroup-specific interaction, at least in the embryonic stage. Furthermore, our results suggest that one Su(Hw) molecule may be interacting with two distant DNA regions through its various zinc fingers. This is not an outlandish claim: Gata3, which has two zinc fingers, has been shown to bind to two GAT half sites separated by a long linker region [Our results support the fact that diversity in regulation is driven in large part by diversity in sequence: the chromatin structure correlates with the different modes and so does sequence conservation. Indeed, r region . Admitteiversity currently does not model the efficiency of the cross-linking or the immunoprecipitation step. Consider a situation where the profiled protein binds a DNA region through a chain of intermediaries. For the region to be reported, each interaction in the chain must get fixed during cross-linking and the antibody should be capable of recognizing the protein when it is part of this complex. Perhaps incorporating the accompanying ChIP binding score in the model will give further insights into the stability of the complexes.Ddiversity uses is the DNA sequence at the ChIP regions, it can make no claim of the function of the identified components; that needs to be validated by separate means, with additional experiments/data.We also note that a ChIP experiment has its own limitations. Phantom peaks biased towards highly expressed regions have been reported in ChIP-seq experiments , 59. Inddiversity is conceptually distinct from standard motif discovery tools, since it asks and answers a very different question. Therefore Diversity does not seek to replace these tools, but it can provide insights in cases where diverse configurations of the same TF are to be detected, specifically from ChIP data. We note that other high-throughput experiments that identify regulatory regions such as active enhancers [diversity limits each sequence to have only one binding site: in other words, one mode is defined by only one motif. For a ChIP-seq experiment that measures a specific TF-DNA interaction, this is a reasonable assumption, but to use diversity on the above mentioned high-throughput experiments the definition of a mode needs to be relaxed to a collection of motifs. We hope to incorporate this in the next version of diversity.The framework of nhancers , accessinhancers , transcrnhancers  will alsS1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigLogo from JASPAR is shown when available.(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S1 File(GZ)Click here for additional data file.