Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Zhao, Yue; Ruan, Shuxiang; Pandey, Manishi; Stormo, Gary D.

doi:10.1534/genetics.112.138685

Cited by 133 publications

(161 citation statements)

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“…This concept has recently been expanded to include dinucleotide features that encode dependencies between adjacent nucleotide positions (e.g., ref. 20). Trinucleotides (21,22) and higher-order k-mer features (23,24), defined as all possible sequences of length k, have also been included in models of DNA sequence specificity.…”

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confidence: 99%

Quantitative modeling of transcription factor binding specificities using DNA shape

Zhou

Shen

Yang

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

235

291

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DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF−DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included crossvalidation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shapeaugmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.protein−DNA recognition | statistical machine learning | support vector regression | protein binding microarray | DNA structure

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confidence: 99%

Quantitative modeling of transcription factor binding specificities using DNA shape

Zhou

Shen

Yang

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

235

291

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“…We consider all sterically allowed configurations of proteins bound to each probe; multiple binding events, overlapping sites, and multiple DNA-bound species (including alternative binding modes of the same TF) are treated rigorously as they do not entail significant computational costs. Thus our framework is more consistent in treating steric exclusion and multiple-species competition for DNA sequence compared with previous biophysical models of protein-DNA energetics: MatrixREDUCE (Foat et al 2006) and BEEML-PBM (Zhao and Stormo 2011;Zhao et al 2012).Moreover, our approach is designed to test a hierarchy of protein-DNA energy models of increasing complexity. We start with a basic mononucleotide model in which the contribution of each nucleotide in the binding site to the total interaction energy is independent of all the other nucleotides.…”

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confidence: 86%

Section: Comparison Of Pbm and Mitomi-based Predictionsmentioning

confidence: 96%

“…To compare the robustness of BindSter predictions across MITOMI and PBM platforms, we have carried out mononucleotide, dinucleotide, and N = 3 k-mer fits for 12 TFs for which both PBM and MITOMI measurements are available (Table S5). We have also compared our results with previously published BEEML-PBM mononucleotide fits (Zhao and Stormo 2011;Zhao et al 2012).One essential difference between BindSter MITOMI and PBM fits is the use of location bias in the latter. The location bias in our model is a quadratic energy contribution with two free parameters per DNA-binding species (Equation 12).…”

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confidence: 87%

“…The MITOMI binding data are thus well suited for computational modeling based on equilibrium statistical mechanics, although the MITOMI approach is less high-throughput than PBMs (Berger et al 2006;Badis et al 2008Badis et al , 2009). Our computational framework differs from previously published biophysical models of protein-DNA interactions (Foat et al 2006;Zhao and Stormo 2011;Zhao et al 2012) in two important aspects: (a) it employs a rigorous procedure in which all sterically allowed configurations of TFs bound to DNA probes are taken into account and (b) it is designed to accommodate a variety of protein-DNA interaction energy models. Thus, in contrast to previous work, BindSter is able to provide a quantitative treatment of overlapping binding sites and multiple DNA-binding species (as may occur if the TF switches between dimeric and monomeric binding or employs distinct binding interfaces to interact with different classes of sites).…”

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confidence: 99%

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A Biophysical Approach to Predicting Protein–DNA Binding Energetics

Locke

Morozov

2015

Genetics

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Sequence-specific interactions between proteins and DNA play a central role in DNA replication, repair, recombination, and control of gene expression. These interactions can be studied in vitro using microfluidics, protein-binding microarrays (PBMs), and other high-throughput techniques. Here we develop a biophysical approach to predicting protein-DNA binding specificities from high-throughput in vitro data. Our algorithm, called BindSter, can model alternative DNA-binding modes and multiple protein species competing for access to DNA, while rigorously taking into account all sterically allowed configurations of DNA-bound factors. BindSter can be used with a hierarchy of protein-DNA interaction models of increasing complexity, including contributions of mononucleotides, dinucleotides, and longer words to the total protein-DNA binding energy. We observe that the quality of BindSter predictions does not change significantly as some of the energy parameters vary over a sizable range. To take this degeneracy into account, we have developed a graphical representation of parameter uncertainties called IntervalLogo. We find that our simplest model, in which each nucleotide in the binding site is treated independently, performs better than previous biophysical approaches. The extensions of this model, in which contributions of longer words are also considered, result in further improvements, underscoring the importance of higher-order effects in protein-DNA energetics. In contrast, we find little evidence of multiple binding modes for the transcription factors (TFs) and experimental conditions in our data set. Furthermore, there is limited consistency in predictions for the same TF based on microfluidics and PBM data.KEYWORDS gene regulation; protein-DNA interactions; thermodynamic modeling; transcription factor S EQUENCE-SPECIFIC interactions between proteins and genomic DNA control numerous cellular processes. An important class of DNA-binding proteins is transcription factors (TFs), which regulate expression of their target genes. Knowledge of in vivo binding locations of TFs is important for reconstructing regulatory networks of gene transcription and for understanding which factors control which genes. These locations can be mapped genome-wide, using chromatin immunoprecipitation followed by microarray hybridization (ChIP-chip) or sequencing (ChIP-seq) (Ren et al. 2000;Park 2009). The latest version of this technique, ChIP-exo, uses exonucleases to trim immunoprecipitated DNA fragments to precise locations of bound proteins, achieving single-base-pair precision (Rhee and Pugh 2011). Many factors influence TF binding locations in living cells, including chromatin structure, which strongly modulates DNA accessibility (Felsenfeld and Groudine 2003), and cooperative or competing interactions with other DNAbinding proteins (Siggers and Gordan 2014). However, the primary determinant of TF binding locations is intrinsic DNA sequence specificity: TFs can recognize and bind their cognate sites on the background of ...

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A preliminary computational outputs versus experimental results: Application of sTRAP, a biophysical tool for the analysis of SNPs of transcription factor‐binding sites

Moradifard

Saghiri

Ehsani

et al. 2020

Molec Gen & Gen Med

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BackgroundIn the human genome, the transcription factors (TFs) and transcription factor‐binding sites (TFBSs) network has a great regulatory function in the biological pathways. Such crosstalk might be affected by the single‐nucleotide polymorphisms (SNPs), which could create or disrupt a TFBS, leading to either a disease or a phenotypic defect. Many computational resources have been introduced to predict the TFs binding variations due to SNPs inside TFBSs, sTRAP being one of them.MethodsA literature review was performed and the experimental data for 18 TFBSs located in 12 genes was provided. The sequences of TFBS motifs were extracted using two different strategies; in the size similar with synthetic target sites used in the experimental techniques, and with 60 bp upstream and downstream of the SNPs. The sTRAP (http://trap.molgen.mpg.de/cgi-bin/trap_two_seq_form.cgi) was applied to compute the binding affinity scores of their cognate TFs in the context of reference and mutant sequences of TFBSs. The alternative bioinformatics model used in this study was regulatory analysis of variation in enhancers (RAVEN; http://www.cisreg.ca/cgi-bin/RAVEN/a). The bioinformatics outputs of our study were compared with experimental data, electrophoretic mobility shift assay (EMSA).ResultsIn 6 out of 18 TFBSs in the following genes COL1A1, Hb ḉᴪ, TF, FIX, MBL2, NOS2A, the outputs of sTRAP were inconsistent with the results of EMSA. Furthermore, no p value of the difference between the two scores of binding affinity under the wild and mutant conditions of TFBSs was presented. Nor, were any criteria for preference or selection of any of the measurements of different matrices used for the same analysis.ConclusionOur preliminary study indicated some paradoxical results between sTRAP and experimental data. However, to link the data of sTRAP to the biological functions, its optimization via experimental procedures with the integration of expanded data and applying several other bioinformatics tools might be required.

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Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Cited by 133 publications

References 58 publications

Quantitative modeling of transcription factor binding specificities using DNA shape

Quantitative modeling of transcription factor binding specificities using DNA shape

A Biophysical Approach to Predicting Protein–DNA Binding Energetics

A preliminary computational outputs versus experimental results: Application of sTRAP, a biophysical tool for the analysis of SNPs of transcription factor‐binding sites

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