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Computational identification of transcriptional regulatory elements in DNA sequence
, 2006
"... Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computatio ..."
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Cited by 55 (0 self)
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Identification and annotation of all the functional elements in the genome, including genes and the regulatory sequences, is a fundamental challenge in genomics and computational biology. Since regulatory elements are frequently short and variable, their identification and discovery using computational algorithms is difficult. However, significant advances have been made in the computational methods for modeling and detection of DNA regulatory elements. The availability of complete genome sequence from multiple organisms, as well as mRNA profiling and high-throughput experimental methods for mapping protein-binding sites in DNA, have contributed to the development of methods that utilize these auxiliary data to inform the detection of transcriptional regulatory elements. Progress is also being made in the identification of cis-regulatory modules and higher order structures of the regulatory sequences, which is essential to the understanding of transcription regulation in the metazoan genomes. This article reviews the computational approaches for modeling and identification of genomic regulatory elements, with an emphasis on the recent developments, and current challenges.
TJ: NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence
- Nucleic Acids Res
"... NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of ..."
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Cited by 55 (1 self)
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NestedMICA is a new, scalable, pattern-discovery system for finding transcription factor binding sites and similar motifs in biological sequences. Like several previous methods, NestedMICA tackles this problem by optimizing a probabilistic mixture model to fit a set of sequences. However, the use of a newly developed inference strategy called Nested Sampling means NestedMICA is able to find optimal solutions without the need for a problematic initialization or seeding step. We investigate the performance of NestedMICA in a range scenario, on synthetic data and a well-characterized set of muscle regulatory regions, and compare it with the popular MEME program. We show that the new method is significantly more sensitive than MEME: in one case, it successfully extracted a target motif from background sequence four times longer than could be handled by the existing program. It also performs robustly on synthetic sequences containing multiple significant motifs. When tested on a real set of regulatory sequences, NestedMICA produced motifs which were good predictors for all five abundant classes of annotated binding sites.
A boosting approach for motif modeling using ChIP-chip data
- Bioinformatics
, 2005
"... Motivation: Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step towards the understanding of gene regulation. Results: This paper describes a boosting approach for modeling TF-DN ..."
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Cited by 44 (8 self)
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Motivation: Building an accurate binding model for a transcription factor (TF) is essential to differentiate its true binding targets from those spurious ones. This is an important step towards the understanding of gene regulation. Results: This paper describes a boosting approach for modeling TF-DNA binding. Different from the widely used weight matrix model, which predicts TF-DNA binding based on a linear combination of position-specific contributions, our approach builds a TF binding classifier by combining a set of weight-matrix-based classifiers, thus yielding a non-linear binding decision rule. The proposed approach is applied to the ChIP-chip data of Saccharomyces cerevisiae. When compared to the weight matrix method, our new approach shows significant improvements on the specificity in a majority of cases. Contact:
A feature-based approach to modeling protein-DNA interactions
- In Proc. RECOMB’07
, 2007
"... Abstract. Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. In many cases this simplify ..."
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Cited by 39 (1 self)
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Abstract. Transcription factor (TF) binding to its DNA target site is a fundamental regulatory interaction. The most common model used to represent TF binding specificities is a position specific scoring matrix (PSSM), which assumes independence between binding positions. In many cases this simplifying assumption does not hold. Here, we present feature motif models (FMMs), a novel probabilistic method for modeling TF-DNA interactions, based on Markov networks. Our approach uses sequence features to represent TF binding specificities, where each feature may span multiple positions. We develop the mathematical formulation of our models, and devise an algorithm for learning their structural features from binding site data. We evaluate our approach on synthetic data, and then apply it to binding site and ChIP-chip data from yeast. We reveal sequence features that are present in the binding specificities of yeast TFs, and show that FMMs explain the binding data significantly better than PSSMs. Key words: transcription factor binding sites, DNA sequence motifs, probabilistic graphical models, Markov networks, motif finder. 1
MotifPrototyper: a Bayesian Profile Model for Motif Families
- Proc. Natl Acad. Sci. USA
, 2004
"... In this paper, we address the problem of modeling generic features of structurally but not textually related DNA motifs, that is, motifs whose consensus sequences are entirely di#erent, but nevertheless share "meta-sequence features" reflecting similarities in the DNA binding domains of ..."
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Cited by 23 (1 self)
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In this paper, we address the problem of modeling generic features of structurally but not textually related DNA motifs, that is, motifs whose consensus sequences are entirely di#erent, but nevertheless share "meta-sequence features" reflecting similarities in the DNA binding domains of their associated protein recognizers. We present MotifPrototyper, a profile Bayesian model which can capture structural properties typical of particular families of motifs. Each family corresponds to transcription regulatory proteins with similar types of structural signatures in their DNA binding domains. We show how to train MotifPrototypers from biologically identified motifs categorized according to the TRANSFAC categorization of transcription factors, and present empirical results of motif classification, motif parameter estimation and de novo motif detection using the learned profile models.
Sequence features of DNA binding sites reveal structural class of associated transcription factor
- Bioinformatics
, 2006
"... Motivation: A key goal in molecular biology is to understand the mechanisms by which a cell regulates the transcription of its genes. One important aspect of this transcriptional regulation is the binding of transcription factors (TFs) to their specific cis-regulatory counterparts on the DNA. TFs re ..."
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Cited by 18 (2 self)
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Motivation: A key goal in molecular biology is to understand the mechanisms by which a cell regulates the transcription of its genes. One important aspect of this transcriptional regulation is the binding of transcription factors (TFs) to their specific cis-regulatory counterparts on the DNA. TFs recognize and bind their DNA counterparts according to the structure of their DNA-binding domains (e.g. zinc finger, leucine zipper, homeodomain). The structure of thesedomains can be used as a basis for grouping TFs into classes. Although the structure of DNAbinding domains varies widely across TFs generally, the TFs within a particular class bind to DNA in a similar fashion, suggesting the existence of class-specific features in the DNA sequences bound by each class of TFs. Results: In this paper, we apply a sparse Bayesian learning algorithm to identify a small set of class-specific features in the DNA sequences bound by different classes of TFs; the algorithm simultaneously learns a true multi-class classifier that uses these features to predict the DNA-binding domain of the TF that recognizes a particular set of DNA sequences. We train our algorithm on the six largest classes in TRANSFAC, comprising a total of 587 TFs. We learn a six-class classifier for this training set that achieves 87 % leave-one-out crossvalidation accuracy. We also identify features within cis-regulatory sequences that are highly specific to each class of TF, which has significant implications for how TF binding sites should be modeled for the purpose of motif discovery.
Extracting sequence features to predict protein-DNA interactions: A comparative study
- Nucleic Acids Research
, 2008
"... Predicting how and where proteins, especially transcription factors (TFs), interact with DNA is an important problem in biology. We present here a systematic study of predictive modeling approaches to the TF-DNA binding problem, which have been frequently shown to be more efficient than those method ..."
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Cited by 15 (3 self)
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Predicting how and where proteins, especially transcription factors (TFs), interact with DNA is an important problem in biology. We present here a systematic study of predictive modeling approaches to the TF-DNA binding problem, which have been frequently shown to be more efficient than those methods only based on position-specific weight matrices (PWMs). In these approaches, a statistical relationship between genomic sequences and gene expression or ChIPbinding intensities is inferred through a regression framework; and influential sequence features are identified by variable selection. We examine a few state-of-the-art learning methods including stepwise linear regression, multivariate adaptive regression splines (MARS), neural networks, support vector machines, boosting, and Bayesian additive regression trees (BART). These methods are applied to both simulated datasets and two whole-genome ChIP-chip datasets on the TFs Oct4 and Sox2, respectively, in human embryonic stem cells. We find that, with proper learning methods, predictive modeling approaches can significantly improve the predictive power and identify more biologically interesting features, such as TF-TF interactions, than the PWM approach. In particular, BART and boosting show the best and the most robust overall performance among all the methods.
Context-specific independence mixture modeling for positional weight matrices
- BIOINFORMATICS, VOL. 22 NO. 14 2006, PAGES E166–E173
, 2006
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CIS: Compound importance sampling method for protein-DNA binding site p-value estimation. Bioinformatics
, 2004
"... Motivation: A key aspect of transcriptional regulation is the binding of transcription factors to sequence-specific binding sites that allow them to modulate the expression of nearby genes. Given models of such binding sites, one can scan regulatory regions for putative binding sites and construct a ..."
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Cited by 9 (1 self)
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Motivation: A key aspect of transcriptional regulation is the binding of transcription factors to sequence-specific binding sites that allow them to modulate the expression of nearby genes. Given models of such binding sites, one can scan regulatory regions for putative binding sites and construct a genome-wide regulatory network. In such genome-wide scans, it is crucial to control the amount of false positive predictions. Recently, several works demonstrated the benefits of modeling dependencies between positions within the binding site. Yet, computing the statistical significance of putative binding sites in this scenario remains a challenge. Results: We present a general, accurate and efficient method for computing p-values of putative binding sites that is applicable to a large class of probabilistic binding site and background models. We demonstrate the accuracy of the method on synthetic and real-life data. Availability: The procedure for scanning DNA sequences and computing the statistical significance of putative binding site scores is available upon request at
