Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells

Wong, Ka-Chun; Li, Yue; Peng, Chengbin

doi:10.1093/bioinformatics/btv555

Cited by 13 publications

(17 citation statements)

References 19 publications

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“…Recent studies identifying the co-occurrence of TF pairs focus either on combinatorial approaches where e.g., specific DNA-sequences bound by different TFs simultaneously were selected from a library of random sequences (Jolma et al, 2015 ) or approaches that focus on data integration e.g., ChIP-seq, SELEX together with Hi-C to reveal long-range chromatin interactions (Jolma et al, 2013 ; Wong et al, 2016 ). Although the selection of interacting TF pairs from a library of random sequences underpins potential interactions of TFs, it does not give any hints on the actual interactions in particular cell types or tissues.…”

Section: Introductionmentioning

confidence: 99%

Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development

et al. 2016

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Transcription factors (TFs) regulate gene expression in living organisms. In higher organisms, TFs often interact in non-random combinations with each other to control gene transcription. Understanding the interactions is key to decipher mechanisms underlying tissue development. The aim of this study was to analyze co-occurring transcription factor binding sites (TFBSs) in a time series dataset from a new cell-culture model of human heart muscle development in order to identify common as well as specific co-occurring TFBS pairs in the promoter regions of regulated genes which can be essential to enhance cardiac tissue developmental processes. To this end, we separated available RNAseq dataset into five temporally defined groups: (i) mesoderm induction stage; (ii) early cardiac specification stage; (iii) late cardiac specification stage; (iv) early cardiac maturation stage; (v) late cardiac maturation stage, where each of these stages is characterized by unique differentially expressed genes (DEGs). To identify TFBS pairs for each stage, we applied the MatrixCatch algorithm, which is a successful method to deduce experimentally described TFBS pairs in the promoters of the DEGs. Although DEGs in each stage are distinct, our results show that the TFBS pair networks predicted by MatrixCatch for all stages are quite similar. Thus, we extend the results of MatrixCatch utilizing a Markov clustering algorithm (MCL) to perform network analysis. Using our extended approach, we are able to separate the TFBS pair networks in several clusters to highlight stage-specific co-occurences between TFBSs. Our approach has revealed clusters that are either common (NFAT or HMGIY clusters) or specific (SMAD or AP-1 clusters) for the individual stages. Several of these clusters are likely to play an important role during the cardiomyogenesis. Further, we have shown that the related TFs of TFBSs in the clusters indicate potential synergistic or antagonistic interactions to switch between different stages. Additionally, our results suggest that cardiomyogenesis follows the hourglass model which was already proven for Arabidopsis and some vertebrates. This investigation helps us to get a better understanding of how each stage of cardiomyogenesis is affected by different combination of TFs. Such knowledge may help to understand basic principles of stem cell differentiation into cardiomyocytes.

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Section: Introductionmentioning

confidence: 99%

Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development

et al. 2016

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“…Another example is RNA splicing, a biological process in which sequences have sub-sequences removed and concatenated [Ule and Blencowe, 2019] (e.g., to convert the word "machine" to "m...ine" to "mine"). Here, fuzzy motifs also cooperate in a distance-dependent fashion, potentially involving very long-range interactions [Wong et al, 2016].…”

Section: Introductionmentioning

confidence: 99%

Learning Distance-Dependent Motif Interactions: An Explicitly Interpretable Neural Model of Genomic Events

Quinn

Nguyen

et al. 2020

Preprint

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In most biological studies, prediction is used primarily to validate the model; the real quest is to understand the underlying phenomenon. Therefore, interpretable deep models for biological studies are required. Here, we propose HyperXPair (the Hyper-parameter eXplainable Motif Pair framework), a new architecture that learns biological motifs and their distance-dependent context through explicitly interpretable parameters that are immediately understood by a biologist. This makes HyperXPair more than a decision-support tool; it is also a hypothesis-generating tool designed to advance knowledge in the field. We demonstrate the utility of our model by learning distance-dependent motif interactions for two biological problems: transcription initiation and RNA splicing.

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“…Several studies pointed out a new venue to explore the characteristics of EP interactions, which suggested that the interaction of transcription factors (TFs) that bind an enhancer and TFs that bind a promoter of an EP pair may contribute to the interaction of this EP pair [2,12,20,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Hundreds of motif pairs may facilitate enhancer-promoter interactions

Wang

2020

Preprint

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Previous studies have shown that pairs of interacting transcription factors (TFs) bind to enhancers and promoters and contribute to their physical interactions. However, to date, we have limited knowledge about these interacting TF pairs. To identify such TF pairs, we systematically studied the co-occurrence of TF-binding motifs in interacting enhancer-promoter (EP) pairs in seven human cell lines. We discovered hundreds of motif pairs that significantly co-occur in enhancers and promoters of interacting EP pairs. We demonstrated that these motif pairs are biologically meaningful and significantly enriched with motif pairs of known interacting TF pairs. We also showed that the identified motif pairs facilitated the discovery of the interacting EP pairs. The predicted motifs and motif pairs are available at http://www.cs.ucf.edu/~xiaoman/ET/EPmotif/.

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Identification of coupling DNA motif pairs on long-range chromatin interactions in human K562 cells

Cited by 13 publications

References 19 publications

Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development

Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development

Learning Distance-Dependent Motif Interactions: An Explicitly Interpretable Neural Model of Genomic Events

Hundreds of motif pairs may facilitate enhancer-promoter interactions

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