Motif discovery and transcription factor binding sites before and after the next-generation sequencing era

Zambelli, Federico; Pesole, Graziano; Pavesi, Giulio

doi:10.1093/bib/bbs016

Cited by 128 publications

(110 citation statements)

References 106 publications

(105 reference statements)

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…Genome-wide datasets have posed several challenges to the computational algorithm design because of increasing complexities of the sequence search space and the requirement of a large amount of memory space. Early methods for genome-wide motif discovery are based on comparative genomic [2] and motif profile search. The comparative genomic method is based on the principle that functional elements (e.g.…”

Section: Introductionmentioning

confidence: 99%

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

Lee

Azizan

Wong

et al. 2018

Biotechnology & Biotechnological Equipment

View full text Add to dashboard Cite

We propose an improved solution to the three-stage DNA motif prediction approach. The threestage approach uses only a subset of input sequences for initial motif prediction, and the initial motifs obtained are employed for site detection in the remaining input subset of non-overlaps. The currently available solution is not robust because motifs obtained from the initial subset are represented as a position weight matrices, which results in high false positives. Our approach, called DeepFinder, employs deep learning neural networks with features associated with binding sites to construct a motif model. Furthermore, multiple prediction tools are used in the initial motif prediction process to obtain a higher number of positive hits. Our features are engineered from the context of binding sites, which are assumed to be enriched with specificity information of sites recognized by transcription factor proteins. DeepFinder is evaluated using several performance metrics on ten chromatin immunoprecipitation (ChIP) datasets. The results show marked improvement of our solution in comparison with the existing solution. This indicates the effectiveness and potential of our proposed DeepFinder for large-scale motif analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

Lee

Azizan

Wong

et al. 2018

Biotechnology & Biotechnological Equipment

View full text Add to dashboard Cite

show abstract

“…With the development of the studies on TFBSs and the accumulation of the experimentally validated TFBSs, the data information on transcription regulation has been greatly enriched [11]. The complexities of these new models are increased, but more information on TFBSs is obtained [12].…”

Section: Introductionmentioning

confidence: 99%

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

Yang²,

Zhang

2017

View full text Add to dashboard Cite

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.

show abstract

“…There have been numerous motif discovery algorithms [4]. They are either approximate or exact, based on whether the algorithm can always fmd all motifs or the optimum motif.…”

Section: Introductionmentioning

confidence: 99%

Reference sequence selection for motif searches

Huo

Zhao

et al. 2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

The planted (I, d) motif search (PMS) is an im portant yet challenging problem in com putational biology. Pattern driven PMS algorithm s usually use k out of t input sequences as reference sequences to generate candidate motifs, and they can find all the (I, d) motifs in the input sequences. However, most of them simply take the first k sequences in the input as reference sequences without elaborate selection processes, and thus they may exhibit sharp fluctuations in running time, especially for large alphabets.In this paper, we build the reference sequence selection problem and propose a method named RefSelect to quickly solve it by evaluating the number of candidate motifs for the reference sequences. RefSelect can bring a practical tim e improvement of the state-of-the-art pattern-driven PMS algorithm s.Experimental results show that RefSelect (1) makes the tested algorithm s solve the PMS problem steadily in an efficient way, (2) particularly, makes them achieve a speedup of up to about 100x on the protein data, and (3) is also suitable for large data sets which contain hundreds or more sequences.

show abstract

Motif discovery and transcription factor binding sites before and after the next-generation sequencing era

Cited by 128 publications

References 106 publications

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

DeepFinder: An integration of feature-based and deep learning approach for DNA motif discovery

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

Reference sequence selection for motif searches

Contact Info

Product

Resources

About