OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif

Drawid, Amar; Gupta, Nupur; Nagaraj, Vijayalakshmi H.; Gélinas, Céline; Sengupta, Anirvan M.

doi:10.1186/1471-2105-10-208

Cited by 13 publications

(14 citation statements)

References 92 publications

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“…By searching the sequences corresponding to these models in genomes with TFBS identification search engines, binding sites and target genes of TFs in the whole genome can be identified. At present, numerous such TFBS identification search engines have been developed, such as position-specific scoring matrix (Stormo 2000), dictionary model (Sabatti et al 2005), artificial neural network (Workman & Stormo 2000), hidden Markov model (Marinescu et al 2005, Drawid et al 2009), Bayesian network (Chen et al 2010), and P-Match (Chekmenev et al 2005).…”

Section: Dsdna Microarraymentioning

confidence: 99%

In vitro DNA-binding profile of transcription factors: methods and new insights

Wang¹,

Lü²,

Gu³

et al. 2011

Journal of Endocrinology

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The DNA-binding specificity of transcription factors (TFs) has broad impacts on cell physiology, cell development and in evolution. However, the DNA-binding specificity of most known TFs still remains unknown. The specificity of a TF protein is determined by its relative affinity to all possible binding sites. In recent years, the development of several in vitro techniques permits high-throughput determination of relative binding affinity of a TF to all possible k bp-long DNA sequences, thus greatly promoting the characterization of DNA-binding specificity of many known TFs. All DNA sequences that can be bound by a TF with various binding affinities form their DNA-binding profile (DBP). The DBP is important to generate an accurate DNA-binding model, identify all DNA-binding sites and target genes of TFs in the whole genome, and build transcription regulatory network. This study reviewed these techniques, especially two master techniques: double-stranded DNA microarray and systematic evolution of ligands by exponential enrichment in combination with parallel DNA sequencing techniques (SELEX-seq).

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Section: Dsdna Microarraymentioning

confidence: 99%

In vitro DNA-binding profile of transcription factors: methods and new insights

Wang¹,

Lü²,

Gu³

et al. 2011

Journal of Endocrinology

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“…Using the TFFMs, the probability of occupancy (Pocc) of a TF within a defined DNA sequence is obtained by multiplying the TFBS probabilities at each position (see Material and Methods section for details). This is a simpler approach than the physico-chemical models used in tools like GOMER [55] and TRAP [56], and somewhat similar in concept to the approach of OHMM [57] which uses HMMs to predict occupancy of TFs with self-overlapping binding motifs. To observe whether Pocc can improve the discriminative power of the TFFMs, Pocc have been computed from TFFMs and assessed for their capacity to discriminate between ChIP-seq data and background sequences and compared to original TFFMs using the best site per ChIP-seq peak to discriminate between ChIP-seq data and background sequences.…”

Section: Resultsmentioning

confidence: 99%

The Next Generation of Transcription Factor Binding Site Prediction

2013

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Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.

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“…HMMs are powerful tools for analyzing sequential data [9,10] that have been adapted to binding site discovery [5,6]. HMMs model a system as a Markov process on internal states that are hidden and cannot be observed directly.…”

Section: Hmms For Binding Site Discoverymentioning

confidence: 99%

“…This points to a major shortcoming of PWM based methodsnamely the inability to learn a threshold directly from data. A major advantage of HMM models over PWM-only approached is that HMMs learn both a PWM, ǫ, and a natural "cutoff" through the chemical potential µ = log z [6]. In terms of the corresponding hardrod model, the probability, P bs (S), for a sequence, S, to be a binding site takes the form of a Fermi-function, If one makes the reasonable assumption that a sequence S is a binding site if P bs (S) > 1/2, we see that µ serves as a natural cut-off for binding site energies [6].…”

Section: B Hmms Position-weight Matrices and Cutoffsmentioning

confidence: 99%

“…A major advantage of HMM models over PWM-only approached is that HMMs learn both a PWM, ǫ, and a natural "cutoff" through the chemical potential µ = log z [6]. In terms of the corresponding hardrod model, the probability, P bs (S), for a sequence, S, to be a binding site takes the form of a Fermi-function, If one makes the reasonable assumption that a sequence S is a binding site if P bs (S) > 1/2, we see that µ serves as a natural cut-off for binding site energies [6]. Thus, the switching probabilities a ij of the HMM can be interpreted as providing a natural cut-off for binding energies through (6).…”

Section: B Hmms Position-weight Matrices and Cutoffsmentioning

confidence: 99%

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Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models

2010

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Hidden Markov Models (HMMs) are a commonly used tool for inference of transcription factor (TF) binding sites from DNA sequence data. We exploit the mathematical equivalence between HMMs for TF binding and the “inverse” statistical mechanics of hard rods in a one-dimensional disordered potential to investigate learning in HMMs. We derive analytic expressions for the Fisher information, a commonly employed measure of confidence in learned parameters, in the biologically relevant limit where the density of binding sites is low. We then use techniques from statistical mechanics to derive a scaling principle relating the specificity (binding energy) of a TF to the minimum amount of training data necessary to learn it.

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OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif

Cited by 13 publications

References 92 publications

In vitro DNA-binding profile of transcription factors: methods and new insights

In vitro DNA-binding profile of transcription factors: methods and new insights

The Next Generation of Transcription Factor Binding Site Prediction

Statistical Mechanics of Transcription-Factor Binding Site Discovery Using Hidden Markov Models

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