Pair Of Motifs Research Articles

TIANA: transcription factors cooperativity inference analysis with neural attention

BackgroundGrowing evidence suggests that distal regulatory elements are essential for cellular function and states. The sequences within these distal elements, especially motifs for transcription factor binding, provide critical information about the underlying regulatory programs. However, cooperativities between transcription factors that recognize these motifs are nonlinear and multiplexed, rendering traditional modeling methods insufficient to capture the underlying mechanisms. Recent development of attention mechanism, which exhibit superior performance in capturing dependencies across input sequences, makes them well-suited to uncover and decipher intricate dependencies between regulatory elements.ResultWe present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared with existing tools, TIANA showed superior interpretability and robust performance in identifying putative transcription factor cooperativities from co-occurring motifs.ConclusionOur results suggest that TIANA can be an effective tool to decipher transcription factor cooperativities from distal sequence data. TIANA can be accessed through: https://github.com/rzzli/TIANA.

Novel Lewis Acid-Base Interactions in Polymer-Derived Sodium-Doped Amorphous Si-B-N Ceramic: Towards Main-Group-Mediated Hydrogen Activation.

Interest is growing in transition metal-free compounds for small molecule activation and catalysis. We discuss the opportunities arising from synthesizing sodium-doped amorphous silicon-boron-nitride (Na-doped a-SiBN). Na+ cations and 3-fold coordinated BIII moieties were incorporated into an amorphous silicon nitride network via chemical modification of a polysilazane followed by pyrolysis in ammonia (NH3) at 1000 °C. Emphasis is placed on the mechanisms of hydrogen (H2) activation within Na-doped a-SiBN structure. This material design approach allows the homogeneous distribution of Na+ and BIII moieties surrounded by SiN4 units contributing to the transformation of the BIII moieties into 4-fold coordinated geometry upon encountering H2, potentially serving as frustrated Lewis acid (FLA) sites. Exposure to H2 induced formation of frustrated Lewis base (FLB) N-= sites with Na+ as a charge-compensating cation, resulting in the in situ formation of a frustrated Lewis pair (FLP) motif (≡BFLA⋅⋅⋅Hδ-⋅⋅⋅Hδ+⋅⋅⋅:N-(Na+)=). Reversible H2 adsorption-desorption behavior with high activation energy for H2 desorption (124 kJ mol-1) suggested the H2 chemisorption on Na-doped a-SiBN. These findings highlight a future landscape full of possibilities within our reach, where we anticipate main-group-mediated small molecule activation will have an important impact on the design of more efficient catalytic processes and the discovery of new catalytic transformations.

The Origin and Evolution of the Paired Animals on Chinese Swords

This article explores the historical evolution of paired animal motifs on Chinese swords, highlighting their transformation from symbols of wealth to emblems of royal power. Originating from Persian textile designs, these motifs entered Chinese culture during the sixth and seventh centuries through Sino-West Asian cultural exchanges. Initially, these motifs reflected exotic tastes and wealth among the Chinese elite. However, with the establishment of the Yuan Dynasty, these motifs were recontextualized as symbols of royal authority. The study traces this cultural journey, examining how Persian artistic traditions were adapted and institutionalized within Chinese imperial iconography, particularly during the Ming and Qing dynasties. Through a detailed analysis of swords and textiles, the paper illustrates the continuity and transformation of these motifs, emphasizing their significance in reflecting broader cultural and political shifts in ancient China.

Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.

The interaction between DNA motifs (DNA motif pairs) influences gene expression through partnership or competition in the process of gene regulation. Potential chromatin interactions between different DNA motifs have been implicated in various diseases. However, current methods for identifying DNA motif pairs rely on the recognition of single DNA motifs or probabilities, which may result in local optimal solutions and can be sensitive to the choice of initial values. A method for precisely identifying DNA motif pairs is still lacking. Here, we propose a novel computational method for predicting DNA Motif Pairs based on Composite Heterogeneous Graph (MPCHG). This approach leverages a composite heterogeneous graph model to identify DNA motif pairs on paired sequences. Compared with the existing methods, MPCHG has greatly improved the accuracy of motifs prediction. Furthermore, the predicted DNA motifs demonstrate heightened DNase accessibility than the background sequences. Notably, the two DNA motifs forming a pair exhibit functional consistency. Importantly, the interacting TF pairs obtained by predicted DNA motif pairs were significantly enriched with known interacting TF pairs, suggesting their potential contribution to chromatin interactions. Collectively, we believe that these identified DNA motif pairs held substantial implications for revealing gene transcriptional regulation under long-range chromatin interactions.

Interpretable prediction of mRNA abundance from promoter sequence using contextual regression models.

While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.

Hydrogen-bonding interactions in 5-fluorocytosine-urea (2/1), 5-fluorocytosine-5-fluorocytosinium 3,5-dinitrosalicylate-water (2/1/1) and 2-amino-4-chloro-6-methylpyrimidine-6-chloronicotinic acid (1/1).

Three new compounds, namely, 5-fluorocytosine-urea (2/1), 2C4H4FN3O·CH4N2O, (I), 5-fluorocytosine-5-fluorocytosinium 3,5-dinitrosalicylate-water (2/1/1), 2C4H4FN3O·C4H5FN3O+·C7H2N2O7-·H2O, (II), and 2-amino-4-chloro-6-methylpyrimidine-6-chloronicotinic acid (1/1), C6H4ClNO2·C5H6ClN3, (III), have been synthesized and characterized by single-crystal X-ray diffraction. In compound (I), 5-fluorocytosine (5FC) molecules A and B form two different homosynthons [R22(8) ring motif], one formed via N-H...O hydrogen bonds and the second via N-H...N hydrogen bonds. In addition to this interaction, a sequence of fused-ring motifs [R21(6), R33(8), R22(8), R43(10) and R22(8)] are formed, generating a supramolecular ladder-like hydrogen-bonded pattern. In compound (II), 5FC and 5-fluorocytosinium are linked by triple hydrogen bonds, generating two fused-ring motifs [R22(8)]. The neutral 5FC and protonated 5-fluorocytosinum cation form a dimeric synthon [R22(8) ring motif] via N-H...O and N-H...N hydrogen bonds. On either side of the dimeric synthon, the neutral 5FC, 5-fluorocytosinium cation, 3,5-dinitrosalicylate anion and water molecule are hydrogen bonded through N-H...O, N-H...N, N-H...OW and OW-HW...O hydrogen bonds, forming a large ring motif [R1010(56)], leading to a three-dimensional supramolecular network. In compound (III), 2-amino-4-chloro-6-methylpyrimidine (ACP) interacts with the carboxylic acid group of 6-chloronicotinic acid via N-H...O and O-H...O hydrogen bonds, generating an R22(8) primary ring motif. Furthermore, the ACP molecules form a base pair via N-H...N hydrogen bonds. The primary motif and base pair combine to form tetrameric units, which are further connected by Cl...Cl interactions. In addition to this hydrogen-bonding interaction, compounds (I) and (III) are further enriched by π-π stacking interactions.

Epitope delimitation: A new method for defining epitopes of human IgG-reactive antigenic peptides based on rabbit-recognized epitope motifs.

The use of precise epitope peptides as antigens is essential for accurate serological diagnosis of viral-infected individuals, but now it remains an unsolvable problem for mapping precise B cell epitopes (BCEs) recognized by human serum. To address this challenge, we propose a novel epitope delimitation (ED) method to uncover BCEs in the delineated human IgG-reactive (HR) antigenic peptides (APs). Specifically, the method based on the rationale of similarities in humoral immune responses between mammalian species consists of a pair of elements: experimentally delineated HR-AP and rabbit-recognized (RR) BCE motif and corresponding pair of sequence alignment analysis. As a result of using the ED approach, after decoding four RR-epitomes of human papillomavirus types 16/18-E6 and E7 proteins utilizing rabbit serum against each recombinant protein and sequence alignment analysis of HR-APs and RR-BCEs, 19 fine BCEs in 17 of 22 known HR-APs were defined based on each corresponding RR-BCE motifs, including the type-specificity of each delimited BCE in homologous proteins. The test with 22 known 16/20mer HR-APs demonstrated that the ED method is effective and efficient, indicating that it can be used as an alternative method to the conventional identification of fine BCEs using overlapping 8mer peptides.

Analyzing the cation-aromatic interactions in proteins: Cation-aromatic database V2.0.

The cation-aromatic database (CAD) is a comprehensive repository of cation-aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation-aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation-π or cation-σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation-aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS-HIS, PHE-ARG, and TRP-ARG pairs exhibited a preferential geometry. The motif pair HIS-HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR-LYS at 10.17%. Conversely, the motif pair TRP-HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation-aromatic interactions in biological molecules. The updated version of CAD (Cation-Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2.

O19 Differential pairing of transmembrane domain GxxxG dimerization motifs generates two HLA-DR conformers defined by selective binding by the mAb Tü36

O19 Differential pairing of transmembrane domain GxxxG dimerization motifs generates two HLA-DR conformers defined by selective binding by the mAb Tü36

CapsNetYY1: identifying YY1-mediated chromatin loops based on a capsule network architecture

BackgroundPrevious studies have identified that chromosome structure plays a very important role in gene control. The transcription factor Yin Yang 1 (YY1), a multifunctional DNA binding protein, could form a dimer to mediate chromatin loops and active enhancer-promoter interactions. The deletion of YY1 or point mutations at the YY1 binding sites significantly inhibit the enhancer-promoter interactions and affect gene expression. To date, only a few computational methods are available for identifying YY1-mediated chromatin loops.ResultsWe proposed a novel model named CapsNetYY1, which was based on capsule network architecture to identify whether a pair of YY1 motifs can form a chromatin loop. Firstly, we encode the DNA sequence using one-hot encoding method. Secondly, multi-scale convolution layer is used to extract local features of the sequence, and bidirectional gated recurrent unit is used to learn the features across time steps. Finally, capsule networks (convolution capsule layer and digital capsule layer) used to extract higher level features and recognize YY1-mediated chromatin loops. Compared with DeepYY1, the only prediction for YY1-mediated chromatin loops, our model CapsNetYY1 achieved the better performance on the independent datasets (AUC > 0.99).ConclusionThe results indicate that CapsNetYY1 is an excellent method for identifying YY1-mediated chromatin loops. We believe that the CapsNetYY1 method will be used for predictive classification of other DNA sequences.

Degeneracy Removal of Spin Bands in Collinear Antiferromagnets with Non-Interconvertible Spin-Structure Motif Pair.

Energy bands in antiferromagnets are supposed to be spin degenerate in the absence of spin-orbit coupling (SOC). Recent studies [Physical Review B 102, 01 4422 (2020)] identified formal symmetry conditions for antiferromagnetic crystals in which this degeneracy can be lifted - spin splitting - even in the vanishing SOC (i.e., non-relativistic) limit. Materials having such symmetries could enable spin-split antiferromagnetic spintronics without the burden of using heavy atom compounds. However, the symmetry conditions that involve spin and magnetic symmetry are not always effective as practical material selection filters. Furthermore, these symmetry conditions do not readily disclose trends in the magnitude and momentum dependence of the spin splitting energy. Here, we show that the formal symmetry conditions enabling spin-split antiferromagnets can be interpreted in terms of local motif pairs, such as octahedra or tetrahedra, each carrying opposite magnetic moments. Collinear antiferromagnets with such spin-structure motif pair whose components interconvert by neither translation nor spatial inversion will show spin splitting. Such a real-space motif-based approach enables an easy way to identify and design materials (illustrated in real example materials) having spin splitting without the need for spin-orbit coupling and offers insights into the momentum dependence and magnitude of the spin splitting. This article is protected by copyright. All rights reserved.

Differential pairing of transmembrane domain GxxxG dimerization motifs defines two HLA-DR MHC class II conformers

MHC class II molecules function to present exogenous antigen-derived peptides to CD4 T cells to both drive T cell activation and to provide signals back into the class II antigen-presenting cell. Previous work established the presence of multiple GxxxG dimerization motifs within the transmembrane domains of MHC class II α and β chains across a wide range of species and revealed a role for differential GxxxG motif pairing in the formation of two discrete mouse class II conformers with distinct functional properties (i.e., M1-and M2-paired I-Ak class II). Biochemical and mutagenesis studies detailed herein extend this model to human class II by identifying an anti-HLA-DR mAb (Tü36) that selectively binds M1-paired HLA-DR molecules. Analysis of the HLA-DR allele reactivity of the Tü36 mAb helped define other HLA-DR residues involved in mAb binding. In silico modeling of both TM domain interactions and whole protein structure is consistent with the outcome of biochemical/mutagenesis studies and provides insight into the possible structural differences between the two HLA-DR conformers. Cholesterol depletion studies indicate a role for cholesterol-rich membrane domains in the formation/maintenance of Tü36 mAb reactive DR molecules. Finally, phylogenetic analysis of the amino acid sequences of Tü36-reactive HLA-DR β chains reveals a unique pattern of both Tü36 mAb reactivity and key amino acid polymorphisms. In total, these studies bring the paradigm M1/M2-paired MHC class II molecules to the human HLA-DR molecule and suggest that the functional differences between these conformers defined in mouse class II extend to the human immune system.

Rheological Properties of Ionically Crosslinked Viscoelastic 2D Films vs. Corresponding 3D Bulk Hydrogels.

Ionically crosslinked hydrogels containing metal coordination motifs have piqued the interest of researchers in recent decades due to their self-healing and adhesive properties. In particular, catechol-functionalized bulk hydrogels have received a lot of attention because of their bioinspired nature. By contrast, very little is known about thin viscoelastic membranes made using similar chelator-ion pair motifs. This shortcoming is surprising because the unique interfacial properties of these membranes, namely, their self-healing and adhesion, would be ideal for capsule shells, adhesives, or for drug delivery purposes. We recently demonstrated the feasibility to fabricate 10 nm thick viscoelastic membranes from catechol-functionalized surfactants that are ionically crosslinked at the liquid/liquid interface. However, it is unclear if the vast know-how existing on the influence of the chelator-ion pair on the mechanical properties of ionically crosslinked three-dimensional (3D) hydrogels can be translated to two-dimensional (2D) systems. To address this question, we compare the dynamic mechanical properties of ionically crosslinked pyrogallol functionalized hydrogels with those of viscoelastic membranes that are crosslinked using the same chelator-ion pairs. We demonstrate that the storage and loss moduli of viscoelastic membranes follow a trend similar to that of the hydrogels, with the membrane becoming stronger as the ion-chelator affinity increases. Yet, membranes relax significantly faster than bulk equivalents. These insights enable the targeted design of viscoelastic, adhesive, self-healing membranes possessing tunable mechanical properties. Such capsules can potentially be used, for example, in cosmetics, as granular inks, or with additional work that includes replacing the fluorinated block by a hydrocarbon-based one in drug delivery and food applications.

Intra-Exon Motif Correlations as a Proxy Measure for Mean Per-Tile Sequence Quality Data in RNA-Seq.

Given the wide variability in the quality of next-generation sequencing data submitted to public repositories, it is essential to identify methods that can perform quality control on these data sets when additional quality control data, such as mean tile data, are missing from public repositories. In this study, we present evidence that correlating counts of reads corresponding to pairs of motifs separated over specific distances on individual exons can be used as a proxy mean tile data in the data sets we analyzed and hence could be used when mean tile data are not available. As test data sets we use the Homo sapiens in vitro transcribed (IVT) data set, and a Drosophila melanogaster data set comprising wild and mutant types. We find that a FastQC analysis of the available parts of these data sets demonstrates that the per-tile sequencing quality is good for all the data sets apart from the mutant-type data where the mutant-r3 data are worse than the mutant-r2 data. Correspondingly, intra-exon motif correlations are reasonably large for all data sets except this latter case where the mutant-r2 correlations are low and the mutant-r3 correlations close to zero. We propose that these extremely low correlations are indicative of bias of technical origin, such as flowcell errors. In addition to this, the intra-exon motif correlations as a function of both guanosine-cytosine (GC) content parameters are somewhat higher and less dependent on the GC content parameters in the IVT-Plasmids messenger RNA (mRNA) selection free RNA-Seq sample (control) than in the other RNA-Seq samples that did undergo mRNA selection: both ribosomal depletion (IVT-Only) and PolyA selection (IVT-PolyA, wild type, and mutant).

A linear CH+–NO3− base pair motif promoted by AgNO3 mediated proton transfer between guanosine and cytidine

Harmonious quaternary interactions consisting of G*–Ag and CH+–NO3− base pairs are disclosed in a mixed hydrogel system.

What Makes a Functional Gene Regulatory Network? A Circuit Motif Analysis.

One of the key questions in systems biology is to understand the roles of gene regulatory circuits in determining cellular states and their functions. In previous studies, some researchers have inferred large gene networks from genome wide genomics/transcriptomics data using the top-down approach, while others have modeled core gene circuits of small sizes using the bottom-up approach. Despite many existing systems biology studies, there is still no general rule on what sizes of gene networks and what types of circuit motifs a system would need to achieve robust biological functions. Here, we adopt a gene circuit motif analysis to discover four-node circuits responsible for multiplicity (rich in dynamical behavior), flexibility (versatile to alter gene expression), or both. We identify the most reoccurring two-node circuit motifs and the co-occurring motif pairs. Furthermore, we investigate the contributing factors of multiplicity and flexibility for large gene networks of different types and sizes. We find that gene networks of intermediate sizes tend to have combined high levels of multiplicity and flexibility. Our study will contribute to a better understanding of the dynamical mechanisms of gene regulatory circuits and provide insights into rational designs of robust gene circuits in synthetic and systems biology.

G-Quadruplex DNA and Other Non-Canonical B-Form DNA Motifs Influence Productive and Latent HIV-1 Integration and Reactivation Potential.

The integration of the HIV-1 genome into the host genome is an essential step in the life cycle of the virus and it plays a critical role in the expression, long-term persistence, and reactivation of HIV expression. To better understand the local genomic environment surrounding HIV-1 proviruses, we assessed the influence of non-canonical B-form DNA (non-B DNA) on the HIV-1 integration site selection. We showed that productively and latently infected cells exhibit different integration site biases towards non-B DNA motifs. We identified a correlation between the integration sites of the latent proviruses and non-B DNA features known to potently influence gene expression (e.g., cruciform, guanine-quadruplex (G4), triplex, and Z-DNA). The reactivation potential of latent proviruses with latency reversal agents also correlated with their proximity to specific non-B DNA motifs. The perturbation of G4 structures in vitro using G4 structure-destabilizing or -stabilizing ligands resulted in a significant reduction in integration within 100 base pairs of G4 motifs. The stabilization of G4 structures increased the integration within 300-500 base pairs from G4 motifs, increased integration near transcription start sites, and increased the proportion of latently infected cells. Moreover, we showed that host lens epithelium-derived growth factor (LEDGF)/p75 and cleavage and polyadenylation specificity factor 6 (CPSF6) influenced the distribution of integration sites near several non-B DNA motifs, especially G4 DNA. Our findings identify non-B DNA motifs as important factors that influence productive and latent HIV-1 integration and the reactivation potential of latent proviruses.

SPACA6 ectodomain structure reveals a conserved superfamily of gamete fusion-associated proteins

SPACA6 is a sperm-expressed surface protein that is critical for gamete fusion during mammalian sexual reproduction. Despite this fundamental role, little is known about how SPACA6 specifically functions. We elucidated the crystal structure of the SPACA6 ectodomain at 2.2-Å resolution, revealing a two-domain protein containing a four-helix bundle and Ig-like β-sandwich connected via a quasi-flexible linker. This structure is reminiscent of IZUMO1, another gamete fusion-associated protein, making SPACA6 and IZUMO1 founding members of a superfamily of fertilization-associated proteins, herein dubbed the IST superfamily. The IST superfamily is defined structurally by its distorted four-helix bundle and a pair of disulfide-bonded CXXC motifs. A structure-based search of the AlphaFold human proteome identified more protein members to this superfamily; remarkably, many of these proteins are linked to gamete fusion. The SPACA6 structure and its connection to other IST-superfamily members provide a missing link in our knowledge of mammalian gamete fusion.

Gnocis: An integrated system for interactive and reproducible analysis and modelling of cis-regulatory elements in Python 3

Gene expression is regulated through cis-regulatory elements (CREs), among which are promoters, enhancers, Polycomb/Trithorax Response Elements (PREs), silencers and insulators. Computational prediction of CREs can be achieved using a variety of statistical and machine learning methods combined with different feature space formulations. Although Python packages for DNA sequence feature sets and for machine learning are available, no existing package facilitates the combination of DNA sequence feature sets with machine learning methods for the genome-wide prediction of candidate CREs. We here present Gnocis, a Python package that streamlines the analysis and the modelling of CRE sequences by providing extensible APIs and implementing the glue required for combining feature sets and models for genome-wide prediction. Gnocis implements a variety of base feature sets, including motif pair occurrence frequencies and the k-spectrum mismatch kernel. It integrates with Scikit-learn and TensorFlow for state-of-the-art machine learning. Gnocis additionally implements a broad suite of tools for the handling and preparation of sequence, region and curve data, which can be useful for general DNA bioinformatics in Python. We also present Deep-MOCCA, a neural network architecture inspired by SVM-MOCCA that achieves moderate to high generalization without prior motif knowledge. To demonstrate the use of Gnocis, we applied multiple machine learning methods to the modelling of D. melanogaster PREs, including a Convolutional Neural Network (CNN), making this the first study to model PREs with CNNs. The models are readily adapted to new CRE modelling problems and to other organisms. In order to produce a high-performance, compiled package for Python 3, we implemented Gnocis in Cython. Gnocis can be installed using the PyPI package manager by running ‘pip install gnocis’. The source code is available on GitHub, at https://github.com/bjornbredesen/gnocis.

Web-MCOT Server for Motif Co-Occurrence Search in ChIP-Seq Data.

(1) Background: The widespread application of ChIP-seq technology requires annotation of cis-regulatory modules through the search of co-occurred motifs. (2) Methods: We present the web server Motifs Co-Occurrence Tool (Web-MCOT) that for a single ChIP-seq dataset detects the composite elements (CEs) or overrepresented homo- and heterotypic pairs of motifs with spacers and overlaps, with any mutual orientations, uncovering various similarities to recognition models within pairs of motifs. The first (Anchor) motif in CEs respects the target transcription factor of the ChIP-seq experiment, while the second one (Partner) can be defined either by a user or a public library of Partner motifs being processed. (3) Results: Web-MCOT computes the significances of CEs without reference to motif conservation and those with more conserved Partner and Anchor motifs. Graphic results show histograms of CE abundance depending on orientations of motifs, overlap and spacer lengths; logos of the most common CE structural types with an overlap of motifs, and heatmaps depicting the abundance of CEs with one motif possessing higher conservation than another. (4) Conclusions: Novel capacities of Web-MCOT allow retrieving from a single ChIP-seq dataset with maximal information on the co-occurrence of motifs and potentiates planning of next ChIP-seq experiments.