From Binding Motifs in Chip-Seq Data to Improved Models of Transcription Factor Binding Sites

Kulakovskiy, Ivan V.; Levitsky, Victor G.; Oshchepkov, Dmitry; Bryzgalov, L. O.; Vorontsov, Ilya E.; Makeev, Vsevolod J.

doi:10.1142/s0219720013400040

Cited by 65 publications

(61 citation statements)

References 22 publications

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“…The second was that they did not consider the relationship of separated binding sites of the same transcription factor. The later improvement on the PWM methods focused on the first one and a number of methods with dinuleotide model [28,29] had been put forward. And then, the k-mer methods [30,31] were developed which had made up for these two disadvantages.…”

Section: Methods Based On Pwm Motifmentioning

confidence: 99%

“…The deep learning methods used here are those deep convolution neural networks. Compared to those previous motif discovery algorithms, such as PWM model [26,[86][87][88][89], dinucleotide model [28,29,[90][91][92] and k-mer model [30,31], the deep convolution neural networks make it possible to extract the long-range dependencies along the sequence.…”

Section: Deep Convolution Neural Network For Identifying the Tfbssmentioning

confidence: 99%

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Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Yang²,

Zhang

2017

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Backgrounds: With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings. Results: We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence. Conclusion: Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

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Section: Methods Based On Pwm Motifmentioning

confidence: 99%

Section: Deep Convolution Neural Network For Identifying the Tfbssmentioning

confidence: 99%

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Yang²,

Zhang

2017

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“…More recently, ChIP-Seq has been developed allowing DNA-protein interactions to be profiled with near base-pair resolution (Kulakovskiy et al 2013). For a detailed explanation of the methodology and a discussion of its challenges and advantages, see Park (2009).…”

Section: Sequencing To Find Dna-protein Interactionsmentioning

confidence: 99%

Cross-Species Analysis of Mouse and Human Cancer Genomes

Robles-Espinoza

Adams

2013

Cold Spring Harb Protoc

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Fundamental advances in our understanding of the human cancer genome have been made over the last five years, driven largely by the development of next-generation sequencing (NGS) technologies. Here we will discuss the tools and technologies that have been used to profile human tumors, how they may be applied to the analysis of the mouse cancer genome, and the results thus far. In addition to mutations that disrupt cancer genes, NGS is also being applied to the analysis of the transcriptome of cancers, and, through the use of techniques such as ChIP-Seq, the protein–DNA landscape is also being revealed. Gaining a comprehensive picture of the mouse cancer genome, at the DNA level and through the analysis of the transcriptome and regulatory landscape, will allow us to “biofilter” for driver genes in more complex human cancers and represents a critical test to determine which mouse cancer models are faithful genetic surrogates of the human disease.

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“…Position weight matrix (PWM) models (Stormo et al, 1982;Staden, 1984) are still the most prevalent representation of TF binding motifs, but several approaches have been proposed recently that employ more complex motif models and may also capture dependencies between motif positions (Grau et al, 2013;Mordelet et al, 2013;Kulakovskiy et al, 2013;Eggeling et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Motif clustering with implications for transcription factor interactions

Grau

Große

Posch

et al. 2015

Preprint

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High-throughput data, for instance ChIP-seq data, measure binding of transcription factors (TFs) or other proteins to DNA and have become a widespread data source for de-novo motif discovery. Often, several ChIP-seq data sets study the same TF under different conditions resulting in several, potentially redundant motifs, which demands for identification and clustering of similar motifs. Here, we propose a refined measure of motif similarity based on the correlation between score profiles on de Bruijn sequences. We demonstrate the utility of the proposed measure in benchmark studies on artificial motifs and motifs discovered from ENCODE ChIP-seq data. We use this measure to cluster motifs discovered from 757 different ENCODE ChIP-seq data sets for 166 TFs and RNA-polymerase II and III. Based on this clustering, we derive a TF interaction network that reflects many known TF-TF interactions, but also reveals novel putative interaction partners.

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From Binding Motifs in Chip-Seq Data to Improved Models of Transcription Factor Binding Sites

Cited by 65 publications

References 22 publications

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

Cross-Species Analysis of Mouse and Human Cancer Genomes

Motif clustering with implications for transcription factor interactions

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