Jury remains out on simple models of transcription factor specificity

Morris, Quaid; Bulyk, Martha L.; Hughes, Timothy R.

doi:10.1038/nbt.1892

Cited by 28 publications

(29 citation statements)

References 6 publications

(8 reference statements)

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“…It has been recently intensively debated in the literature that, in light of the availability of high-throughput protein–DNA-binding affinity data, whether there is a need to develop more sophisticated models or simpler position weight matrices are sufficient to capture such binding landscape (60,74). In this work, we demonstrated that kmerHMM can capture multiple binding modes of a DNA-binding protein, for which a single position weight matrix model is unable to do.…”

Section: Discussionmentioning

confidence: 99%

DNA motif elucidation using belief propagation

Wong

Chan

Peng

et al. 2013

Nucleic Acids Research

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Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors’ websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

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Section: Discussionmentioning

confidence: 99%

DNA motif elucidation using belief propagation

Wong

Chan

Peng

et al. 2013

Nucleic Acids Research

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“…For example, most forkhead factors (e.g., Foxa2 and Foxo3) can bind both a primary forkhead motif (RYAAAYA) and a secondary motif (AHAACA) (R = A or G, Y = C or T, H = A, C, or T) (Badis et al, 2009;Mariani, Weinand, Vedenko, Barrera, & Bulyk, 2017). While the overall prevalence of this phenomenon among TFs is debated, it is clear that for some TF classes, such diversity in the types of DNA sequences they can recognize is genuine (Morris, Bulyk, & Hughes, 2011;Siggers et al, 2012;Zhao & Stormo, 2011).…”

Section: Representations Of Binding Specificitymentioning

confidence: 99%

Diversification of transcription factor–DNA interactions and the evolution of gene regulatory networks

Rogers

Bulyk

2018

WIREs Mechanisms of Disease

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Sequence-specific transcription factors (TFs) bind short DNA sequences in the genome to regulate the expression of target genes. In the last decade, numerous technical advances have enabled the determination of the DNA-binding specificities of many of these factors. Large-scale screens of many TFs enabled the creation of databases of TF DNA-binding specificities, typically represented as position weight matrices (PWMs). Although great progress has been made in determining and predicting binding specificities systematically, there are still many surprises to be found when studying a particular TF's interactions with DNA in detail. Paralogous TFs' binding specificities can differ in subtle ways, in a manner that is not immediately apparent from looking at their PWMs. These differences affect gene regulatory outputs and enable TFs to rewire transcriptional networks over evolutionary time. This review discusses recent observations made in the study of TF-DNA interactions that highlight the importance of continued in-depth analysis of TF-DNA interactions and their inherent complexity. This article is categorized under: Biological Mechanisms > Regulatory Biology.

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“…Protein binding microarray technology [22] offers an experimental approach to measure binding strength of TFs, but the compression of all possible 10-mers in the array produces a convoluted signal that is not trivial to decode. The proper solution to this problem remains under debate [32], [33]. Alternatively, we use a computational method to obtain TF binding preferences based on structural information from TF-DNA complexes.…”

Section: Resultsmentioning

confidence: 99%

The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

et al. 2013

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Gene regulatory networks show robustness to perturbations. Previous works identified robustness as an emergent property of gene network evolution but the underlying molecular mechanisms are poorly understood. We used a multi-tier modeling approach that integrates molecular sequence and structure information with network architecture and population dynamics. Structural models of transcription factor-DNA complexes are used to estimate relative binding specificities. In this model, mutations in the DNA cause changes on two levels: (a) at the sequence level in individual binding sites (modulating binding specificity), and (b) at the network level (creating and destroying binding sites). We used this model to dissect the underlying mechanisms responsible for the evolution of robustness in gene regulatory networks. Results suggest that in sparse architectures (represented by short promoters), a mixture of local-sequence and network-architecture level changes are exploited. At the local-sequence level, robustness evolves by decreasing the probabilities of both the destruction of existent and generation of new binding sites. Meanwhile, in highly interconnected architectures (represented by long promoters), robustness evolves almost entirely via network level changes, deleting and creating binding sites that modify the network architecture.

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Jury remains out on simple models of transcription factor specificity

Cited by 28 publications

References 6 publications

DNA motif elucidation using belief propagation

DNA motif elucidation using belief propagation

Diversification of transcription factor–DNA interactions and the evolution of gene regulatory networks

The Underlying Molecular and Network Level Mechanisms in the Evolution of Robustness in Gene Regulatory Networks

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