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Journal articles on the topic 'Prediction of transcription factor binding sites'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Jun-tao Guo, Shane Lofgren, and Alvin Farrel. "Structure-based prediction of transcription factor binding sites." Tsinghua Science and Technology 19, no. 6 (December 2014): 568–77. http://dx.doi.org/10.1109/tst.2014.6961027.

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2

Wang, Guohua, Fang Wang, Qian Huang, Yu Li, Yunlong Liu, and Yadong Wang. "Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/757530.

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Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

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3

Talebzadeh, Mohammad, and Fatemeh Zare-Mirakabad. "Transcription Factor Binding Sites Prediction Based on Modified Nucleosomes." PLoS ONE 9, no. 2 (February 21, 2014): e89226. http://dx.doi.org/10.1371/journal.pone.0089226.

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4

VON ROHR, PETER, MARKUS T. FRIBERG, and HAJA N. KADARMIDEEN. "PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING GENETICAL GENOMICS METHODS." Journal of Bioinformatics and Computational Biology 05, no. 03 (June 2007): 773–93. http://dx.doi.org/10.1142/s0219720007002680.

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In this paper, we wanted to test whether it is possible to use genetical genomics information such as expression quantitative trait loci (eQTL) mapping results as input to a transcription factor binding site (TFBS) prediction algorithm. Furthermore, this new approach was compared to the more traditional cluster based TFBS prediction. The results of eQTL mapping are used as input to one of the top ranking TFBS prediction algorithms. Genes with observed expression profiles showing the same eQTL region are collected into eQTL groups. The promoter sequences of all the genes within the same eQTL group are used as input in the transcription factor binding site search. This approach is tested with a real data set of a recombinant inbred line population of Arabidopsis thaliana. The predicted motifs are compared to results obtained from the conventional approach of first clustering the gene expression values and then using the promoter sequences of the genes within the same cluster as input for the transcription factor binding site prediction. Our eQTL based approach produced different motifs compared to the cluster based method. Furthermore the score of the eQTL based motifs was higher than the score of the cluster based motifs. In a comparison to already predicted motifs from the AtcisDB database, the eQTL based and the cluster based method produced about the same number of hits with binding sites from AtcisDB. In conclusion, the results of this study clearly demonstrate the usefulness of eQTL to predict transcription factor binding sites.

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Li, Hongyang, Daniel Quang, and Yuanfang Guan. "Anchor: trans-cell type prediction of transcription factor binding sites." Genome Research 29, no. 2 (December 19, 2018): 281–92. http://dx.doi.org/10.1101/gr.237156.118.

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6

Li, X., S. Zhong, and W. H. Wong. "Reliable prediction of transcription factor binding sites by phylogenetic verification." Proceedings of the National Academy of Sciences 102, no. 47 (November 14, 2005): 16945–50. http://dx.doi.org/10.1073/pnas.0504201102.

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7

Yi, Xianfu, Yu-Dong Cai, Zhisong He, WeiRen Cui, and Xiangyin Kong. "Prediction of Nucleosome Positioning Based on Transcription Factor Binding Sites." PLoS ONE 5, no. 9 (September 1, 2010): e12495. http://dx.doi.org/10.1371/journal.pone.0012495.

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8

Zhong, Shan, Xin He, and Ziv Bar-Joseph. "Predicting tissue specific transcription factor binding sites." BMC Genomics 14, no. 1 (2013): 796. http://dx.doi.org/10.1186/1471-2164-14-796.

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9

Mahmoud, Maiada M., Nahla A. Belal, and Aliaa Youssif. "Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1." Applied Sciences 11, no. 11 (May 31, 2021): 5123. http://dx.doi.org/10.3390/app11115123.

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Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.

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10

FRIBERG, MARKUS T. "PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES USING ChIP-chip AND PHYLOGENETIC FOOTPRINTING DATA." Journal of Bioinformatics and Computational Biology 05, no. 01 (February 2007): 105–16. http://dx.doi.org/10.1142/s0219720007002540.

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We present an algorithm for predicting transcription factor binding sites based on ChIP-chip and phylogenetic footprinting data. Our algorithm is robust against low promoter sequence similarity and motif rearrangements, because it does not depend on multiple sequence alignments. This, in turn, allows us to incorporate information from more distant species. Representative random data sets are used to estimate the score significance. Our algorithm is fully automatic, and does not require human intervention. On a recent S. cerevisiae data set, it achieves higher accuracy than the previously best algorithms. Adaptive ChIP-chip threshold and the modular positional bias score are two general features of our algorithm that increase motif prediction accuracy and could be implemented in other algorithms as well. In addition, since our algorithm works partly orthogonally to other algorithms, combining several algorithms can increase prediction accuracy even further. Specifically, our method finds 6 motifs not found by the 2nd best algorithm.

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11

TOWSEY, MICHAEL W., JAMES J. GORDON, and JAMES M. HOGAN. "THE PREDICTION OF BACTERIAL TRANSCRIPTION START SITES USING SVMS." International Journal of Neural Systems 16, no. 05 (October 2006): 363–70. http://dx.doi.org/10.1142/s0129065706000767.

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Identifying promoters is the key to understanding gene expression in bacteria. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). In this paper, we address the problem of predicting transcription start sites in Escherichia coli. Knowing the TSS position, one can then predict the promoter position to within a few base pairs, and vice versa. The accepted method for promoter prediction is to use a pair of position weight matrices (PWMs), which define conserved motifs at the sigma-factor binding site. However this method is known to result in a large number of false positive predictions, thereby limiting its usefulness to the experimental biologist. We adopt an alternative approach based on the Support Vector Machine (SVM) using a modified mismatch spectrum kernel. Our modifications involve tagging the motifs with their location, and selectively pruning the feature set. We quantify the performance of several SVM models and a PWM model using a performance metric of area under the detection-error tradeoff (DET) curve. SVM models are shown to outperform the PWM on a biologically realistic TSS prediction task. We also describe a more broadly applicable peak scoring technique which reduces the number of false positive predictions, greatly enhancing the utility of our results.

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12

Ardakani, Fatemeh Behjati, Florian Schmidt, and Marcel H. Schulz. "Predicting transcription factor binding using ensemble random forest models." F1000Research 7 (October 4, 2018): 1603. http://dx.doi.org/10.12688/f1000research.16200.1.

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Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).

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Behjati Ardakani, Fatemeh, Florian Schmidt, and Marcel H. Schulz. "Predicting transcription factor binding using ensemble random forest models." F1000Research 7 (September 2, 2019): 1603. http://dx.doi.org/10.12688/f1000research.16200.2.

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Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier built based upon data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).

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14

Zeng, Yuanqi, Meiqin Gong, Meng Lin, Dongrui Gao, and Yongqing Zhang. "A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning." IEEE Access 8 (2020): 219256–74. http://dx.doi.org/10.1109/access.2020.3042903.

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15

Won, Kyoung-Jae, Bing Ren, and Wei Wang. "Genome-wide prediction of transcription factor binding sites using an integrated model." Genome Biology 11, no. 1 (2010): R7. http://dx.doi.org/10.1186/gb-2010-11-1-r7.

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16

Casilli, R., A. Marongiu, S. Melchionna, P. Palazzari, R. Paparcone, and V. Rosato. "IMAGE: A New Tool for the Prediction of Transcription Factor Binding Sites." Bioinformatics and Biology Insights 2 (January 2008): 117793220800200. http://dx.doi.org/10.1177/117793220800200004.

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IMAGE is an application tool, based on the vector quantization method, aiding the discovery of nucleotidic sequences corresponding to Transcription Factor binding sites. Starting from the knowledge of regulation regions of a number of co-expressed genes, the software is able to predict the occurrence of specific motifs of different lengths (starting from 6 base pairs) with a defined number of punctual mutations.

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17

Grau, J., I. Ben-Gal, S. Posch, and I. Grosse. "VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees." Nucleic Acids Research 34, Web Server (July 1, 2006): W529—W533. http://dx.doi.org/10.1093/nar/gkl212.

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18

Song, Jia, Li Xu, and Hong Sun. "Efficient Identification of Transcription Factor Binding Sites with a Graph Theoretic Approach." Computational and Mathematical Methods in Medicine 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/856281.

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Identifying transcription factor binding sites with experimental methods is often expensive and time consuming. Although many computational approaches and tools have been developed for this problem, the prediction accuracy is not satisfactory. In this paper, we develop a new computational approach that can model the relationships among all short sequence segments in the promoter regions with a graph theoretic model. Based on this model, finding the locations of transcription factor binding site is reduced to computing maximum weighted cliques in a graph with weighted edges. We have implemented this approach and used it to predict the binding sites in two organisms,Caenorhabditis elegansandmus musculus. We compared the prediction accuracy with that of the Gibbs Motif Sampler. We found that the accuracy of our approach is higher than or comparable with that of the Gibbs Motif Sampler for most of tested data and can accurately identify binding sites in cases where the Gibbs Motif Sampler has difficulty to predict their locations.

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19

Reddy, Timothy E., Charles DeLisi, and Boris E. Shakhnovich. "Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites." PLoS Computational Biology 3, no. 5 (May 11, 2007): e90. http://dx.doi.org/10.1371/journal.pcbi.0030090.

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20

Reddy, Timothy E., Charles P. DeLisi, and Boris Shakhnovich. "Binding Site Graphs: A New Graph Theoretical Framework for Prediction of Transcription Factor Binding Sites." PLoS Computational Biology preprint, no. 2007 (2005): e90. http://dx.doi.org/10.1371/journal.pcbi.0030090.eor.

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21

Ramsey, Stephen A., Theo A. Knijnenburg, Kathleen A. Kennedy, Daniel E. Zak, Mark Gilchrist, Elizabeth S. Gold, Carrie D. Johnson, et al. "Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites." Bioinformatics 26, no. 17 (July 27, 2010): 2071–75. http://dx.doi.org/10.1093/bioinformatics/btq405.

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22

Zhang, Shaoqiang, Shan Li, Phuc T. Pham, and Zhengchang Su. "Simultaneous prediction of transcription factor binding sites in a group of prokaryotic genomes." BMC Bioinformatics 11, no. 1 (2010): 397. http://dx.doi.org/10.1186/1471-2105-11-397.

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23

Guturu, Harendra, Andrew C. Doxey, Aaron M. Wenger, and Gill Bejerano. "Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1632 (December 19, 2013): 20130029. http://dx.doi.org/10.1098/rstb.2013.0029.

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Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis -regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF–TF–DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein–protein interactions, potentially indirect interactions and ‘through-DNA’ interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex .

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ABNIZOVA, IRINA, ALISTAIR G. RUST, MARK ROBINSON, RENE TE BOEKHORST, and WALTER R. GILKS. "TRANSCRIPTION BINDING SITE PREDICTION USING MARKOV MODELS." Journal of Bioinformatics and Computational Biology 04, no. 02 (April 2006): 425–41. http://dx.doi.org/10.1142/s0219720006001813.

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One of the main goals of analysing DNA sequences is to understand the temporal and positional information that specifies gene expression. An important step in this process is the recognition of gene expression regulatory elements. Experimental procedures for this are slow and costly. In this paper we present a computational non-supervised algorithm that facilitates the process by statistically identifying the most likely regions within a putative regulatory sequence. A probabilistic technique is presented, based on the approximation of regulatory DNA with a Markov chain, for the location of putative transcription factor binding sites in a single stretch of DNA. Hereto we developed a procedure to approximate the order of Markov model for a given DNA sequence that circumvents some of the prohibitive assumptions underlying Markov modeling. Application of the algorithm to data from 55 genes in five species shows the high sensitivity of this Markov search algorithm. Our algorithm does not require any prior knowledge in the form of description or cross-genomic comparison; it is context sensitive and takes DNA heterogeneity into account.

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25

Yang, Jinyu, Anjun Ma, Adam D. Hoppe, Cankun Wang, Yang Li, Chi Zhang, Yan Wang, Bingqiang Liu, and Qin Ma. "Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework." Nucleic Acids Research 47, no. 15 (August 2, 2019): 7809–24. http://dx.doi.org/10.1093/nar/gkz672.

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Abstract The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein–DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-regulatory motif prediction using deep neural networks and the binomial distribution model. DESSO outperformed existing tools, including DeepBind, in predicting motifs in 690 human ENCODE ChIP-sequencing datasets. Furthermore, the deep-learning framework of DESSO expanded motif discovery beyond the state-of-the-art by allowing the identification of known and new protein–protein–DNA tethering interactions in human transcription factors (TFs). Specifically, 61 putative tethering interactions were identified among the 100 TFs expressed in the K562 cell line. In this work, the power of DESSO was further expanded by integrating the detection of DNA shape features. We found that shape information has strong predictive power for TF–DNA binding and provides new putative shape motif information for human TFs. Thus, DESSO improves in the identification and structural analysis of TF binding sites, by integrating the complexities of DNA binding into a deep-learning framework.

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Rahi, Sahand J., Peter Virnau, Leonid A. Mirny, and Mehran Kardar. "Predicting transcription factor specificity with all-atom models." Nucleic Acids Research 36, no. 19 (October 1, 2008): 6209–17. http://dx.doi.org/10.1093/nar/gkn589.

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Abstract The binding of a transcription factor (TF) to a DNA operator site can initiate or repress the expression of a gene. Computational prediction of sites recognized by a TF has traditionally relied upon knowledge of several cognate sites, rather than an ab initio approach. Here, we examine the possibility of using structure-based energy calculations that require no knowledge of bound sites but rather start with the structure of a protein–DNA complex. We study the PurR Escherichia coli TF, and explore to which extent atomistic models of protein–DNA complexes can be used to distinguish between cognate and noncognate DNA sites. Particular emphasis is placed on systematic evaluation of this approach by comparing its performance with bioinformatic methods, by testing it against random decoys and sites of homologous TFs. We also examine a set of experimental mutations in both DNA and the protein. Using our explicit estimates of energy, we show that the specificity for PurR is dominated by direct protein–DNA interactions, and weakly influenced by bending of DNA.

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Hawkins, J., C. Grant, W. S. Noble, and T. L. Bailey. "Assessing phylogenetic motif models for predicting transcription factor binding sites." Bioinformatics 25, no. 12 (May 28, 2009): i339—i347. http://dx.doi.org/10.1093/bioinformatics/btp201.

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28

Endres, Robert G., Thomas C. Schulthess, and Ned S. Wingreen. "Toward an atomistic model for predicting transcription-factor binding sites." Proteins: Structure, Function, and Bioinformatics 57, no. 2 (June 11, 2004): 262–68. http://dx.doi.org/10.1002/prot.20199.

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29

Prestridge, Dan S. "Predicting Pol II Promoter Sequences using Transcription Factor Binding Sites." Journal of Molecular Biology 249, no. 5 (June 1995): 923–32. http://dx.doi.org/10.1006/jmbi.1995.0349.

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30

Wu, Wei-Sheng. "Different Functional Gene Clusters in Yeast have Different Spatial Distributions of the Transcription Factor Binding Sites." Bioinformatics and Biology Insights 5 (January 2011): BBI.S6362. http://dx.doi.org/10.4137/bbi.s6362.

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Transcription factors control gene expression by binding to short specific DNA sequences, called transcription factor binding sites (TFBSs), in the promoter of a gene. Thus, studying the spatial distribution of TFBSs in the promoters may provide insights into the molecular mechanisms of gene regulation. I developed a method to construct the spatial distribution of TFBSs for any set of genes of interest. I found that different functional gene clusters have different spatial distributions of TFBSs, indicating that gene regulation mechanisms may be very different among different functional gene clusters. I also found that the binding sites for different transcription factors (TFs) may have different spatial distributions: a sharp peak, a plateau or no dominant single peak. The spatial distributions of binding sites for many TFs derived from my analyses are valuable prior information for TFBS prediction algorithm because different regions of a promoter can assign different possibilities for TFBS occurrence.

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31

Rao, J. Sunil, Suresh Karanam, Colleen D. McCabe, and Carlos S. Moreno. "Genomic Promoter Analysis Predicts Functional Transcription Factor Binding." Advances in Bioinformatics 2008 (October 30, 2008): 1–9. http://dx.doi.org/10.1155/2008/369830.

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Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%. Conclusion. Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.

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32

Xiaobao SU, and Lifang LIU. "Computational Prediction of Transcription Factor Binding Sites Based on HMM Model and Information Content." International Journal of Digital Content Technology and its Applications 5, no. 10 (October 31, 2011): 152–59. http://dx.doi.org/10.4156/jdcta.vol5.issue10.18.

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Ambesi-Impiombato, Alberto, Mukesh Bansal, Pietro Liò, and Diego di Bernardo. "Computational framework for the prediction of transcription factor binding sites by multiple data integration." BMC Neuroscience 7, Suppl 1 (2006): S8. http://dx.doi.org/10.1186/1471-2202-7-s1-s8.

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Liu, Zhijie, Jun-Tao Guo, Ting Li, and Ying Xu. "Structure-based prediction of transcription factor binding sites using a protein-DNA docking approach." Proteins: Structure, Function, and Bioinformatics 72, no. 4 (March 4, 2008): 1114–24. http://dx.doi.org/10.1002/prot.22002.

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35

Whitington, Tom, Andrew C. Perkins, and Timothy L. Bailey. "High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites." Nucleic Acids Research 37, no. 1 (November 6, 2008): 14–25. http://dx.doi.org/10.1093/nar/gkn866.

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36

Zhao, Yuming, Fang Wang, Su Chen, Jun Wan, and Guohua Wang. "Methods of MicroRNA Promoter Prediction and Transcription Factor Mediated Regulatory Network." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/7049406.

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MicroRNAs (miRNAs) are short (~22 nucleotides) noncoding RNAs and disseminated throughout the genome, either in the intergenic regions or in the intronic sequences of protein-coding genes. MiRNAs have been proved to play important roles in regulating gene expression. Hence, understanding the transcriptional mechanism of miRNA genes is a very critical step to uncover the whole regulatory network. A number of miRNA promoter prediction models have been proposed in the past decade. This review summarized several most popular miRNA promoter prediction models which used genome sequence features, or other features, for example, histone markers, RNA Pol II binding sites, and nucleosome-free regions, achieved by high-throughput sequencing data. Some databases were described as resources for miRNA promoter information. We then performed comprehensive discussion on prediction and identification of transcription factor mediated microRNA regulatory networks.

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Deng, Lei, Hui Wu, Xuejun Liu, and Hui Liu. "DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence." International Journal of Molecular Sciences 22, no. 11 (May 24, 2021): 5521. http://dx.doi.org/10.3390/ijms22115521.

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Predicting in vivo protein–DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been identified by biochemical experiments that are time-consuming and laborious. To tackle this challenge, many computational methods have been proposed to predict TF binding sites from DNA sequence. Although previous methods have achieved remarkable performance in the prediction of protein–DNA interactions, there is still considerable room for improvement. In this paper, we present a hybrid deep learning framework, termed DeepD2V, for transcription factor binding sites prediction. First, we construct the input matrix with an original DNA sequence and its three kinds of variant sequences, including its inverse, complementary, and complementary inverse sequence. A sliding window of size k with a specific stride is used to obtain its k-mer representation of input sequences. Next, we use word2vec to obtain a pre-trained k-mer word distributed representation model. Finally, the probability of protein–DNA binding is predicted by using the recurrent and convolutional neural network. The experiment results on 50 public ChIP-seq benchmark datasets demonstrate the superior performance and robustness of DeepD2V. Moreover, we verify that the performance of DeepD2V using word2vec-based k-mer distributed representation is better than one-hot encoding, and the integrated framework of both convolutional neural network (CNN) and bidirectional LSTM (bi-LSTM) outperforms CNN or the bi-LSTM model when used alone. The source code of DeepD2V is available at the github repository.

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Le, Daniel D., Tyler C. Shimko, Arjun K. Aditham, Allison M. Keys, Scott A. Longwell, Yaron Orenstein, and Polly M. Fordyce. "Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding." Proceedings of the National Academy of Sciences 115, no. 16 (March 27, 2018): E3702—E3711. http://dx.doi.org/10.1073/pnas.1715888115.

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Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF–DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

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Mukhin, A. M., V. G. Levitsky, and S. A. Lashin. "Developing of WebMCOT Web-Service for Finding Cooperative Site-Binding TF DNA-Motifs." Vestnik NSU. Series: Information Technologies 17, no. 4 (2019): 74–86. http://dx.doi.org/10.25205/1818-7900-2019-17-4-5-74-86.

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Regulation of eukaryotic gene transcription is controlled by specific proteins transcription factors. Transcription factors bind certain regions of genomic DNA (binding sites or motives). Common action of two or more transcription factors is widespread mechanism of transcription factor action. Hence, the term ‘composite element’ implied two closely located and frequently occurred in genomic DNA motives. Composite elements are partitioned onto those with two overlapped motifs, or with these two motifs separated with a spacer. Currently, the chromatin immunoprecipitation high throughput approach ChIP-seq is used to locate binding sites for a certain “anchor” transcription factor in vivo in genomic scale. Thus, the search of composite elements with the help of ChIP-seq whole-genome transcription factor binding profiles is the actual bioinformatics issue. But existing approaches for prediction of composite elements on the basis of ChIP-seq data either omit an overlap of motifs (but require only a single ChIP-seq dataset) or consider an overlap of motifs (but require additional ChIP-seq data for a partner motif). But, ChIP-seq experiments are very expensive. In the Institute of Cytology and Genetics, MCOT program has been recently developed. It performs search of motifs taking into account their overlaps based on a single ChIP-seq dataset. MCOT is a console application and does not have many user friendly functions like data preparation and report generation. This work presents a web service WebMCOT for prediction of co-occurred DNA motifs in ChIP-seq data. WebMCOT consists of three parts: client, server, and worker. Software tools list, architecture and web interface are presented.

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Gearing, Linden J., Helen E. Cumming, Ross Chapman, Alexander M. Finkel, Isaac B. Woodhouse, Kevin Luu, Jodee A. Gould, Samuel C. Forster, and Paul J. Hertzog. "CiiiDER: A tool for predicting and analysing transcription factor binding sites." PLOS ONE 14, no. 9 (September 4, 2019): e0215495. http://dx.doi.org/10.1371/journal.pone.0215495.

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Gunewardena, Sumedha, Peter Jeavons, and Zhaolei Zhang. "Enhancing the Prediction of Transcription Factor Binding Sites by Incorporating Structural Properties and Nucleotide Covariations." Journal of Computational Biology 13, no. 4 (May 2006): 929–45. http://dx.doi.org/10.1089/cmb.2006.13.929.

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Hooghe, Bart, Stefan Broos, Frans van Roy, and Pieter De Bleser. "A flexible integrative approach based on random forest improves prediction of transcription factor binding sites." Nucleic Acids Research 40, no. 14 (April 5, 2012): e106-e106. http://dx.doi.org/10.1093/nar/gks283.

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Broos, Stefan, Arne Soete, Bart Hooghe, Raymond Moran, Frans van Roy, and Pieter De Bleser. "PhysBinder: improving the prediction of transcription factor binding sites by flexible inclusion of biophysical properties." Nucleic Acids Research 41, W1 (April 24, 2013): W531—W534. http://dx.doi.org/10.1093/nar/gkt288.

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Salama, R. A., and D. J. Stekel. "A non-independent energy-based multiple sequence alignment improves prediction of transcription factor binding sites." Bioinformatics 29, no. 21 (August 28, 2013): 2699–704. http://dx.doi.org/10.1093/bioinformatics/btt463.

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Lee, Wook, Byungkyu Park, and Kyungsook Han. "Sequence-Based Prediction of Putative Transcription Factor Binding Sites in DNA Sequences of Any Length." IEEE/ACM Transactions on Computational Biology and Bioinformatics 15, no. 5 (September 1, 2018): 1461–69. http://dx.doi.org/10.1109/tcbb.2017.2773075.

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Kim, Gi Bae, Ye Gao, Bernhard O. Palsson, and Sang Yup Lee. "DeepTFactor: A deep learning-based tool for the prediction of transcription factors." Proceedings of the National Academy of Sciences 118, no. 2 (December 28, 2020): e2021171118. http://dx.doi.org/10.1073/pnas.2021171118.

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A transcription factor (TF) is a sequence-specific DNA-binding protein that modulates the transcription of a set of particular genes, and thus regulates gene expression in the cell. TFs have commonly been predicted by analyzing sequence homology with the DNA-binding domains of TFs already characterized. Thus, TFs that do not show homologies with the reported ones are difficult to predict. Here we report the development of a deep learning-based tool, DeepTFactor, that predicts whether a protein in question is a TF. DeepTFactor uses a convolutional neural network to extract features of a protein. It showed high performance in predicting TFs of both eukaryotic and prokaryotic origins, resulting in F1 scores of 0.8154 and 0.8000, respectively. Analysis of the gradients of prediction score with respect to input suggested that DeepTFactor detects DNA-binding domains and other latent features for TF prediction. DeepTFactor predicted 332 candidate TFs in Escherichia coli K-12 MG1655. Among them, 84 candidate TFs belong to the y-ome, which is a collection of genes that lack experimental evidence of function. We experimentally validated the results of DeepTFactor prediction by further characterizing genome-wide binding sites of three predicted TFs, YqhC, YiaU, and YahB. Furthermore, we made available the list of 4,674,808 TFs predicted from 73,873,012 protein sequences in 48,346 genomes. DeepTFactor will serve as a useful tool for predicting TFs, which is necessary for understanding the regulatory systems of organisms of interest. We provide DeepTFactor as a stand-alone program, available at https://bitbucket.org/kaistsystemsbiology/deeptfactor.

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Maienschein-Cline, Mark, Aaron R. Dinner, William S. Hlavacek, and Fangping Mu. "Improved predictions of transcription factor binding sites using physicochemical features of DNA." Nucleic Acids Research 40, no. 22 (August 24, 2012): e175-e175. http://dx.doi.org/10.1093/nar/gks771.

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Feroz, Khan, Sharma Richa, Shukla Rakesh Kumar, Meena Abha, Shasany Ajit Kumar, and Sharma Ashok. "Genomic Identification of SinR Transcription Factor Binding Sites in Nitrogen Fixing Bacterium Bradyrhizobium japonicum." Open Bioinformatics Journal 3, no. 1 (May 5, 2009): 8–17. http://dx.doi.org/10.2174/1875036200903010008.

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SinR is a transcription factor which controls expression of stress tolerance sin genes related to alternate development processes under stress condition. Identification of genome wide SinR-box motif and their regulated genes has not been worked out yet in Bradyrhizobium japonicum. For this, a weight matrix of 9 bp was developed from the known promoter sequences of Bacillus subtilis, which was then used for genome wide identification of co-regulated genes. The methodology first involves phylogenetic footprinting of SinR regulated genes and then construction of scoring matrix through ‘Consensus’ and confirmation through MEME & D-Matrix tools. Genomic prediction was done through ‘Patser’ program and confirmation through ‘PossumSearch’ program in Linux system. Results showed that all the 371 predicted genes belongs to 9 different functional classes, in which 221 found in operons with more than 80% Sin-box motif similarity. Similar approach can be used in other bacteria to explore hidden genomic regulatory network.

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Hosseini, Shahrbanou, Armin Otto Schmitt, Jens Tetens, Bertram Brenig, Henner Simianer, Ahmad Reza Sharifi, and Mehmet Gültas. "In Silico Prediction of Transcription Factor Collaborations Underlying Phenotypic Sexual Dimorphism in Zebrafish (Danio rerio)." Genes 12, no. 6 (June 7, 2021): 873. http://dx.doi.org/10.3390/genes12060873.

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The transcriptional regulation of gene expression in higher organisms is essential for different cellular and biological processes. These processes are controlled by transcription factors and their combinatorial interplay, which are crucial for complex genetic programs and transcriptional machinery. The regulation of sex-biased gene expression plays a major role in phenotypic sexual dimorphism in many species, causing dimorphic gene expression patterns between two different sexes. The role of transcription factor (TF) in gene regulatory mechanisms so far has not been studied for sex determination and sex-associated colour patterning in zebrafish with respect to phenotypic sexual dimorphism. To address this open biological issue, we applied bioinformatics approaches for identifying the predicted TF pairs based on their binding sites for sex and colour genes in zebrafish. In this study, we identified 25 (e.g., STAT6-GATA4; JUN-GATA4; SOX9-JUN) and 14 (e.g., IRF-STAT6; SOX9-JUN; STAT6-GATA4) potentially cooperating TFs based on their binding patterns in promoter regions for sex determination and colour pattern genes in zebrafish, respectively. The comparison between identified TFs for sex and colour genes revealed several predicted TF pairs (e.g., STAT6-GATA4; JUN-SOX9) are common for both phenotypes, which may play a pivotal role in phenotypic sexual dimorphism in zebrafish.

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Pudimat, R., E. G. Schukat-Talamazzini, and R. Backofen. "A multiple-feature framework for modelling and predicting transcription factor binding sites." Bioinformatics 21, no. 14 (May 19, 2005): 3082–88. http://dx.doi.org/10.1093/bioinformatics/bti477.

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