Evaluation of methods for modeling transcription factor sequence specificity

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Diversity and Complexity in DNA Recognition by Transcription Factors

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GHT-SELEX demonstrates unexpectedly high intrinsic sequence specificity and complex DNA binding of many human transcription factors

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BACH1 Binding Links the Genetic Risk for Severe Periodontitis with ST8SIA1

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By the company they keep: interaction networks define the binding ability of transcription factors

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Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

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Neural networks with circular filters enable data efficient inference of sequence motifs

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MEDEA: analysis of transcription factor binding motifs in accessible chromatinPreprint

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LMMO: A Large Margin Approach for Refining Regulatory Motifs

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Evaluating a linear k-mer model for protein-DNA interactions using high-throughput SELEX data

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Transcription factor motif quality assessment requires systematic comparative analysis

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High-throughput identification of functional regulatory SNPs in systemic lupus erythematosus

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DNA Motif Recognition Modeling from Protein Sequences

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Transcription factor motif quality assessment requires systematic comparative analysis

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NoPeak: k-mer-based motif discovery in ChIP-Seq data without peak calling

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Evaluation of deep learning approaches for modeling transcription factor sequence specificity

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Benchmarking transcription factor binding site prediction models: a comparative analysis on synthetic and biological data

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Cross-platform motif discovery and benchmarking to explore binding specificities of poorly studied human transcription factors

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Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning

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Structural insights into DNA recognition by the BEN domain of the transcription factor BANP

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Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence

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Quantifying Intensities of Transcription Factor-DNA Binding by Learning From an Ensemble of Protein Binding Microarrays

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Codebook: sequence specificity and genomic binding of poorly-characterized human transcription factorsPreprint

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Predicting CTCF cell type active binding sites in human genome

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Characterization of non-coding variants associated with transcription-factor binding through ATAC-seq-defined footprint QTLs in liver

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A Biophysical Approach to Predicting Protein–DNA Binding Energetics

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Motif Discovery via Convolutional Networks with K-mer Embedding

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Deep learning for HGT insertion sites recognition

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The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

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Predicting In-Vitro DNA-Protein Binding With a Spatially Aligned Fusion of Sequence and Shape

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MARZ: an algorithm to combinatorially analyze gapped n-mer models of transcription factor binding

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Learning regulatory motifs by direct optimization of Fisher Exact Test Score

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The geometric influence on the Cys2His2 zinc finger domain and functional plasticity

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A structure-based Multiple-Instance Learning approach to predicting in vitrotranscription factor-DNA interaction

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Protein–DNA force assay in a microfluidic format

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Identification of a regulatory pathway governing TRAF1 via an arthritis-associated non-coding variant

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The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes

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Trinucleotide Base Pair Stacking Free Energy for Understanding TF-DNA Recognition and the Functions of SNPs

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abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis

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Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model

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Using Chou’s 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature

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A deterministic code for transcription factor-DNA recognition through computation of binding interfaces

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Toward Identification of Functional Sequences and Variants in Noncoding DNA

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Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome

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A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factor Binding Sites

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A systematic survey of TF function in E. coli suggests RNAP stabilization is a prevalent strategy for both repressors and activators

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Deciphering transcription factors and their corresponding regulatory elements during inhibitory interneuron differentiation using deep neural networks

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Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism

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Dissection of integrated readout reveals the structural thermodynamics of DNA selection by transcription factors

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CLIMP: Clustering Motifs via Maximal Cliques with Parallel Computing Design

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Contribution of nonconsensus base pairs within ArsR binding sequences toward ArsR-DNA binding and arsenic-mediated transcriptional induction

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Deep Omics

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BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing

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Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode

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Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data

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An expanded codebook of human transcription factor DNA-binding specificity

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MotifMark: Finding regulatory motifs in DNA sequences

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Design of Large-Scale Reporter Construct Arrays for Dynamic, Live Cell Systems Biology

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Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid–Binding Proteins

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TRAFICA: an open chromatin language model to improve transcription factor binding affinity prediction

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Transcriptional Regulators: Dynamic Drivers of Multicellular Formation, Cell Differentiation and Development

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DNA Structure Helps Predict Protein Binding

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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme

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DynaMIT: the dynamic motif integration toolkit

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GNet: An integrated context-aware neural framework for transcription factor binding signal at single nucleotide resolution prediction

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ProNet DB: a proteome-wise database for protein surface property representations and RNA-binding profiles

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DeepCTF: transcription factor binding specificity prediction using DNA sequence plus shape in an attention-based deep learning model

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Hierarchical Attention Network for Predicting DNA-Protein Binding Sites

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DIpartite: A tool for detecting bipartite motifs by considering base interdependencies

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Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination

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Interactomes – Scaffolds of Cellular Systems

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Identifying a novel Mecp2-mediated epigenetic mechanism controlling Lonp1 in the hippocampus and its disruption by aging

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Inferring binding specificities of human transcription factors with the wisdom of crowds

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Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space

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Accurate affinity models for SH2 domains from peptide binding assays and free‐energy regression

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Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification

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Interpretable protein-DNA interactions captured by structure-sequence optimization

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Transcription factor binding specificities of the oomycete Phytophthora infestans reflect conserved and divergent evolutionary patterns and predict function

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ShapeME: A Tool and Web Front-end for De Novo Discovery of Structural Motifs Underpinning Protein-DNA Interactions

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The jigsaw puzzle of sequence phenotype inference: Piecing together Shannon entropy, importance sampling, and Empirical Bayes

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Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression

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Shapely DNA attracts the right partner

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Exploring comprehensive within-motif dependence of transcription factor binding in Escherichia coli

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

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ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks

Koo, P. K., Ploenzke, M., Anand, P., Paul, S. B., & Majdandzic, A. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2768-6_12 · Cited by 2

Deciphering the Role of the Ser‐Phosphorylation Pattern on the DNA‐Binding Activity of Max Transcription Factor Using Chemical Protein Synthesis

Nithun, R. V., Yao, Y. M., Lin, X., Habiballah, S., Afek, A., & Jbara, M. (2023) · Angewandte Chemie · doi:10.1002/ange.202310913 · Cited by 2

On the abundance and importance of AXXXA sequence motifs in globular proteins and their involvement in CβCβ interaction

Tajane, S. V., Thakur, A., Acharya, S., Chakrabarti, P., & Dey, S. (2024) · Journal of Structural Biology · doi:10.1016/j.jsb.2024.108129 · Cited by 2

OptimDase: An Algorithm for Predicting DNA Binding Sites with Combined Feature Encoding

Liu, Z., Liu, J. S., Wei, D., Man, R., Jiang, J., Zhang, B., Li, L., & Zhao, Z. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00704-8 · Cited by 2

DynaMIT: the dynamic motif integration toolkit

Dassi, E., & Quattrone, A. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw119 · Cited by 2

ASSESSMENT OF REGRESSION METHODS FOR INFERENCE OF REGULATORY NETWORKS INVOLVED IN CIRCADIAN REGULATION

Aderhold, A., Dirk Husmeier, A., Smith, V. A., Millar, A. J., & Marco Grzegorczyk, B. (2013) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · Cited by 2

Learning a Hybrid Architecture for Sequence Regression and Annotation

Zhang, Y., Henao, R., Carin, L., Zhong, J., & Hartemink, A. J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10145 · Cited by 2

Bayesian Multi-scale Convolutional Neural Network for Motif Occupancy Identification

Li, W., Zhao, Q., Zhang, H., Quan, X., Xu, J., & Yin, Y. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313556 · Cited by 2

Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions

Aderhold, A., Smith, V. A., & Husmeier, D. (2015) · Wiley eBooks · doi:10.1002/9781119078845.ch27 · Cited by 2

A comprehensive fitness landscape model reveals the evolutionary history and future evolvability of eukaryotic cis -regulatory DNA sequencesPreprint

Vaishnav, E. D., de Boer, C. G., Yassour, M., Molinet, J., Fan, L., Adiconis, X., Thompson, D. A., Cubillos, F. A., Levin, J. Z., & Regev, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.17.430503 · Cited by 2

Stability selection for regression-based models of transcription factor–DNA binding specificity

Mordelet, F., Horton, J., Hartemink, A. J., Engelhardt, B. E., & Gordân, R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt221 · Cited by 56

Transcription factor motif quality assessment requires systematic comparative analysis

Kibet, C. K., & Machanick, P. (2016) · F1000Research · doi:10.12688/f1000research.7408.2 · Cited by 11

Inferring binding specificities of human transcription factors with the wisdom of crowds

Gryzunov, N., Penzar, D., Kamenets, V., Vyaltsev, V., Kozin, I., Eliseeva, I. A., Nozdrin, V., Vorontsov, I. E., Bushuev, S., Strekalovskikh, V. V., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.118025 · Cited by 3

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Schröder, A., Eichner, J., Supper, J., Eichner, J., Wanke, D., Henneges, C., & Zell, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013876 · Cited by 15

Maximally Efficient Modeling of DNA Sequence Motifs at All Levels of Complexity

Stormo, G. D. (2011) · Genetics · doi:10.1534/genetics.110.126052 · Cited by 41