The Motif Tool Assessment Platform (MTAP) for Sequence-Based Transcription Factor Binding Site Prediction Tools

Quest, Daniel J.; Ali, Hesham

doi:10.1007/978-1-60761-854-6_8

Cited by 3 publications

(2 citation statements)

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“…Computational techniques have been developed to augment our knowledge about TF binding specificities and, currently, there are close to 200 sequence-based ( 9 ) and around 17 structure-based ( 10 ) algorithms in the literature. Sequence-based methods exploit statistical ( 11 , 12 ) or enumerative approaches to identify TF binding sites from ChIP-chip, ChIP-seq, promoter or genomic sequences ( 13 – 16 ).…”

Section: Introductionmentioning

confidence: 99%

“…Sequence-based methods exploit statistical ( 11 , 12 ) or enumerative approaches to identify TF binding sites from ChIP-chip, ChIP-seq, promoter or genomic sequences ( 13 – 16 ). The prediction accuracies of nine of these sequence-based algorithms were compared on TF binding data sets from RegulonDB ( 17 ) using the Motif Tool for Assessment Platform, showing similar performances ( 9 , 18 ). Structure-based algorithms take advantage of known 3D structures of TF-DNA complexes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

Pujato

Kieken

Skiles

et al. 2014

Nucleic Acids Research

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Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

Pujato

Kieken

Skiles

et al. 2014

Nucleic Acids Research

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HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics

Ali

Ricci

Epicoco

et al. 2012

2012 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Logic minimization and rule extraction for identification of functional sites in molecular sequences

2012

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BackgroundLogic minimization is the application of algebraic axioms to a binary dataset with the purpose of reducing the number of digital variables and/or rules needed to express it. Although logic minimization techniques have been applied to bioinformatics datasets before, they have not been used in classification and rule discovery problems. In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sites in proteins. TFBS are important in various developmental processes and glycosylation is a posttranslational modification critical to protein functions.MethodsIn the present study, we first transformed the original biological dataset into a suitable binary form. Logic minimization was then applied to generate sets of simple rules to describe the transformed dataset. These rules were used to predict TFBS and O-glycosylation sites. The TFBS dataset is obtained from the TRANSFAC database, while the glycosylation dataset was compiled using information from OGLYCBASE and the Swiss-Prot Database.We performed the same predictions using two standard classification techniques, Artificial Neural Networks (ANN) and Support Vector Machines (SVM), and used their sensitivities and positive predictive values as benchmarks for the performance of our proposed algorithm. SVM were also used to reduce the number of variables included in the logic minimization approach.ResultsFor both TFBS and O-glycosylation sites, the prediction performance of the proposed logic minimization method was generally comparable and, in some cases, superior to the standard ANN and SVM classification methods with the advantage of providing intelligible rules to describe the datasets. In TFBS prediction, logic minimization produced a very small set of simple rules. In glycosylation site prediction, the rules produced were also interpretable and the most popular rules generated appeared to correlate well with recently reported hydrophilic/hydrophobic enhancement values of amino acids around possible O-glycosylation sites. Experiments with Self-Organizing Neural Networks corroborate the practical worth of the logic minimization method for these case studies.ConclusionsThe proposed logic minimization algorithm provides sets of rules that can be used to predict TFBS and O-glycosylation sites with sensitivity and positive predictive value comparable to those from ANN and SVM. Moreover, the logic minimization method has the additional capability of generating interpretable rules that allow biological scientists to correlate the predictions with other experimental results and to form new hypotheses for further investigation. Additional experiments with alternative rule-extraction techniques demonstrate that the logic minimization method is able to produce accurate rules from datasets with large numbers of variables and limited numbers of positive examples.

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The Motif Tool Assessment Platform (MTAP) for Sequence-Based Transcription Factor Binding Site Prediction Tools

Cited by 3 publications

References 20 publications

Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

HPCS 2012 tutorials: Tutorial I: High performance computing in biomedical informatics

Logic minimization and rule extraction for identification of functional sites in molecular sequences

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