Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument

Nutiu, Razvan; Friedman, Robin; Luo, Shujun; Khrebtukova, Irina; Schwartz, David A.; Li, Robin; Zhang, Lu; Schroth, Gary P.; Burge, Christopher B.

doi:10.1038/nbt.1882

Cited by 190 publications

(220 citation statements)

References 31 publications

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“…Some TF families are more likely to require interaction models than others. In particular the bZIP and bHLH families are commonly fitted much better by including adjacent dinucleotide energy contributions, consistent with previous information (Berger et al 2006;Maerkl and Quake 2007;Stormo and Zhao 2007;Zhao et al 2009;Nutiu et al 2011).…”

Section: Discussionsupporting

confidence: 61%

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

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Identifying transcription factor (TF) binding sites is essential for understanding regulatory networks. The specificity of most TFs is currently modeled using position weight matrices (PWMs) that assume the positions within a binding site contribute independently to binding affinity for any site. Extensive, high-throughput quantitative binding assays let us examine, for the first time, the independence assumption for many TFs. We find that the specificity of most TFs is well fit with the simple PWM model, but in some cases more complex models are required. We introduce a binding energy model (BEM) that can include energy parameters for nonindependent contributions to binding affinity. We show that in most cases where a PWM is not sufficient, a BEM that includes energy parameters for adjacent dinucleotide contributions models the specificity very well. Having more accurate models of specificity greatly improves the interpretation of in vivo TF localization data, such as from chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments.

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Section: Discussionsupporting

confidence: 61%

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

et al. 2012

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“…The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques [2][3][4][5] . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 .…”

Section: A N a Ly S I Smentioning

confidence: 99%

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

et al. 2015

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Knowing the sequence specificities of DNA-and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence.DNA-and RNA-binding proteins play a central role in gene regulation, including transcription and alternative splicing. The sequence specificities of a protein are most commonly characterized using position weight matrices 1 (PWMs), which are easy to interpret and can be scanned over a genomic sequence to detect potential binding sites. However, growing evidence indicates that sequence specificities can be more accurately captured by more complex techniques 2-5 . Recently, 'deep learning' has achieved record-breaking performance in a variety of information technology applications 6,7 . We adapted deep learning methods to the task of predicting sequence specificities and found that they compete favorably with the state of the art. Our approach, called DeepBind, is based on deep convolutional neural networks and can discover new patterns even when the locations of patterns within sequences are unknown-a task for which traditional neural networks require an exorbitant amount of training data.There are several challenging aspects in learning models of sequence specificity using modern high-throughput technologies. First, the data come in qualitatively different forms. Protein binding microarrays (PBMs) 8 and RNAcompete assays 9 provide a specificity coefficient for each probe sequence, whereas chromatin immunoprecipitation (ChIP)-seq 10 provides a ranked list of putatively bound sequences of varying length, and HT-SELEX 11 generates a set of very high affinity sequences. Second, the quantity of data is large. A typical high-throughput experiment measures between 10,000 and 100,000 sequences, and it is computationally demanding to incorporate them all. Third, each data acquisition technology has its own artifacts, biases and limitations, and we must discover the pertinent specificities despite these unwanted effects. For example, ChIP-seq reads often localize to "hyper-ChIPable" regions of the genome near highly expressed genes 12 .DeepBind (Fig. 1) addresses the above challenges. (i) It can be applied to both microarray and sequencing data; (ii) it can learn from millions of...

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“…The principal goals of these methods are to establish the genomic binding locations of TFs and to determine their consensus binding sequences, position weight matrixes, and binding energy landscapes (32,41). Absolute affinities can be acquired with only a few high-throughput methods (17,(42)(43)(44), and kinetic information on protein-DNA interactions has so far only come from low-throughput, complex, and tedious methods such as electrophoretic mobility shift assays (EMSA) (45,46), SPR (18), isothermal titration calorimetry (ITC) (47), and single molecule experiments (48). Completely defining the kinetic parameters of TF-DNA interactions would provide a better understanding of how TF binding to promoters is integrated and translated into transcriptional output.…”

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confidence: 99%

mentioning

confidence: 99%

Massively parallel measurements of molecular interaction kinetics on a microfluidic platform

Geertz

Shore

Maerkl

2012

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

103

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Quantitative biology requires quantitative data. No high-throughput technologies exist capable of obtaining several hundred independent kinetic binding measurements in a single experiment. We present an integrated microfluidic device (k-MITOMI) for the simultaneous kinetic characterization of 768 biomolecular interactions. We applied k-MITOMI to the kinetic analysis of transcription factor (TF)-DNA interactions, measuring the detailed kinetic landscapes of the mouse TF Zif268, and the yeast TFs Tye7p, Yox1p, and Tbf1p. We demonstrated the integrated nature of k-MITOMI by expressing, purifying, and characterizing 27 additional yeast transcription factors in parallel on a single device. Overall, we obtained 2,388 association and dissociation curves of 223 unique molecular interactions with equilibrium dissociation constants ranging from 2 × 10 −6 M to 2 × 10 −9 M, and dissociation rate constants of approximately 6 s −1 to 8.5 × 10 −3 s −1 . Association rate constants were uniform across 3 TF families, ranging from 3.7 × 10 6 M −1 s −1 to 9.6 × 10 7 M −1 s −1 , and are well below the diffusion limit. We expect that k-MITOMI will contribute to our quantitative understanding of biological systems and accelerate the development and characterization of engineered systems.biochemistry | biophysics | systems biology S ystems and synthetic biology, as well as the computational models and engineering-based approaches they employ, rely heavily on quantitative data (1, 2). Thus far, efforts in systems biology have mainly focused on cataloging and mapping genomes and proteomes. Genome sequencing and gene expression analysis have provided insight into genome architecture (3-6), and functional genomics approaches, including high-throughput protein-based methods (7-15), mapped network topologies.Although the number of known protein-protein and protein-DNA interactions is already substantial, the information describing such networks is predominantly qualitative and binary in nature. It is also becoming clear that network topologies alone are not sufficient to model complex biological processes. Precise quantitative information describing every interaction in a network would be tremendously valuable (2), yet binding affinities are known for only a small fraction of interactions (16, 17) and kinetic information hardly exists at all. This dearth of quantitative interaction data is due to a lack of high-throughput technologies capable of measuring kinetic rates of biomolecular interactions. Current methods used for kinetic rate measurements are generally based on surface plasmon resonance (SPR) (18) such as BioRad's ProteOn XPR36 6 × 6 array system, which measures 36 interactions in a single run. SPR-based measurements have been integrated with microfluidic devices (19, 20) to achieve higher degrees of parallelization (21). Yet proof of concept demonstrations have made use of only a small fraction of the proposed throughput and often restrict their measurements to protein-antibody interactions with high affinities and long half-lives...

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Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument

Cited by 190 publications

References 31 publications

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Improved Models for Transcription Factor Binding Site Identification Using Nonindependent Interactions

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Massively parallel measurements of molecular interaction kinetics on a microfluidic platform

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