Cited by 382Genomics and Chromatin DynamicsGenomics and Phylogenetic StudiesGene Regulatory Network Analysis

The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Evaluation of methods for modeling transcription factor sequence specificity

Open access

DREAM5 Consortium, Weirauch, Matthew T, Cote, Atina, Norel, Raquel, Annala, Matti, Zhao, Yue, Riley, Todd R, Saez-Rodriguez, Julio, Cokelaer, Thomas, Vedenko, Anastasia, Talukder, Shaheynoor, Bussemaker, Harmen J, Morris, Quaid D, Bulyk, Martha L, Stolovitzky, Gustavo, Hughes, Timothy R·2013·Nature Biotechnology

DOI 10.1038/nbt.2486PMC PMC3687085

Open this paper in CoChat — free account

Abstract

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/nbt.2486

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Pfam Protein Families Database
Bateman, A. G. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.276 · Cited by 14,075
WebLogo: A Sequence Logo Generator: Figure 1Preprint
Crooks, G. E., Hon, G. C., Chandonia, J., & Brenner, S. E. (2004) · Genome Research · doi:10.1101/gr.849004 · Cited by 12,418
Fitting a mixture model by expectation maximization to discover motifs in biopolymers.
Bailey, T. L., & Elkan, C. (1994) · PubMed · Cited by 4,982
Sequence logos: a new way to display consensus sequences
Schneider, T. D., & Stephens, R. M. (1990) · Nucleic Acids Research · doi:10.1093/nar/18.20.6097 · Cited by 3,354
The Pfam protein families database
Finn, R. D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J. E., Gavin, O. L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp985 · Cited by 2,694
MEME-ChIP: motif analysis of large DNA datasets
Machanick, P., & Bailey, T. L. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr189 · Cited by 1,815
A NEW GENERATION OF HOMOLOGY SEARCH TOOLS BASED ON PROBABILISTIC INFERENCE
Eddy, S. R. (2009) · International Conference on Genome Informatics (GIW) · doi:10.1142/9781848165632_0019 · Cited by 1,275
NCBI GEO: archive for functional genomics data sets--10 years on
Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K., Sherman, P. M., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1184 · Cited by 1,127
Diversity and Complexity in DNA Recognition by Transcription Factors
Badis, G., Berger, M., Philippakis, A. A., Talukder, S., Gehrke, A. R., Jaeger, S., Chan, E. T., Metzler, G., Vedenko, A., Chen, X., et al. (2009) · Science · doi:10.1126/science.1162327 · Cited by 1,031
Self-citation

Flagged first, then by citations

Show all 59 references

Sorted by citations

Items 101–200 of 382

High-Resolution Specificity from DNA Sequencing Highlights Alternative Modes of Lac Repressor Binding
Zuo, Z., & Stormo, G. D. (2014) · Genetics · doi:10.1534/genetics.114.170100 · Cited by 46
A general approach for discriminative de novo motif discovery from high-throughput data
Grau, J., Posch, S., Große, I., & Keilwagen, J. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt831 · Cited by 48
Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape
Dai, H., Umarov, R. K., Kuwahara, H., Li, Y., Song, L., & Gao, X. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx480 · Cited by 52
Protein–DNA binding in high-resolution
Mahony, S., & Pugh, B. F. (2015) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409238.2015.1051505 · Cited by 48
Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites
Swindell, W. R., Sarkar, M. K., Stuart, P. E., Voorhees, J. J., Elder, J. T., Johnston, A., & Gudjonsson, J. E. (2015) · Clinical and Translational Medicine · doi:10.1186/s40169-015-0054-5 · Cited by 46
High-Throughput Affinity Measurements of Transcription Factor and DNA Mutations Reveal Affinity and Specificity Determinants
Aditham, A. K., Markin, C. J., Mokhtari, D. A., DelRosso, N. V., & Fordyce, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.11.012 · Cited by 45
Fast motif matching revisited: high-order PWMs, SNPs and indels
Korhonen, J. H., Palin, K., Taipale, J., & Ukkonen, E. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw683 · Cited by 45
Non-base-contacting residues enable kaleidoscopic evolution of metazoan C2H2 zinc finger DNA binding
Najafabadi, H. S., Garton, M., Weirauch, M. T., Mnaimneh, S., Yang, A. W. H., Kim, P. M., & Hughes, T. R. (2017) · Genome biology · doi:10.1186/s13059-017-1287-y · Cited by 47
Self-citation
An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data
Liu, B., Yang, J., Li, Y., McDermaid, A., & Ma, Q. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx026 · Cited by 42
Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds
Mariani, L., Weinand, K., Vedenko, A., Barrera, L. A., & Bulyk, M. L. (2017) · Cell Systems · doi:10.1016/j.cels.2017.06.015 · Cited by 41
Self-citation
Cis-regulatory elements and human evolution
Siepel, A. C., & Arbiza, L. (2014) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2014.08.011 · Cited by 45
Single-molecule states link transcription factor binding to gene expression
Doughty, B. R., Hinks, M. M., Schaepe, J. M., Marinov, G. K., Thurm, A. R., Rios-Martinez, C., Parks, B. E., Tan, Y., Marklund, E., Dubocanin, D., et al. (2024) · Nature · doi:10.1038/s41586-024-08219-w · Cited by 42
Learning the Regulatory Code of Gene Expression
Zrimec, J., Buric, F., Kokina, M., García, V., & Zelezniak, A. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.673363 · Cited by 45
Single-Cell Analyses of ESCs Reveal Alternative Pluripotent Cell States and Molecular Mechanisms that Control Self-Renewal
Papatsenko, D., Darr, H., Kulakovskiy, I. V., Waghray, A., Makeev, V. J., MacArthur, B. D., & Lemischka, I. R. (2015) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.07.004 · Cited by 44
Spec-seq: determining protein-DNA-binding specificity by sequencing
Stormo, G. D., Zuo, Z., & Chang, Y. K. (2014) · Briefings in Functional Genomics · doi:10.1093/bfgp/elu043 · Cited by 42
BEESEM: estimation of binding energy models using HT-SELEX data
Ruan, S., Swamidass, S. J., & Stormo, G. D. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx191 · Cited by 42
A Review About Transcription Factor Binding Sites Prediction Based on Deep Learning
Zeng, Y., Gong, M., Meng, L., Gao, D., & Zhang, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3042903 · Cited by 42
Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation
Hafezqorani, S., Lafzi, A., de Bruin, R. G., van Zonneveld, A. J., van der Veer, E. P., Son, Y. A., & Kazan, H. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw048 · Cited by 41
A unified approach for quantifying and interpreting DNA shape readout by transcription factors
Rube, H. T., Rastogi, C., Kribelbauer-Swietek, J. F., & Bussemaker, H. J. (2018) · Molecular Systems Biology · doi:10.15252/msb.20177902 · Cited by 40
Self-citation
True equilibrium measurement of transcription factor-DNA binding affinities using automated polarization microscopy
Jung, C., Bandilla, P., von Reutern, M., Schnepf, M., Rieder, S., Unnerstall, U., & Gaul, U. (2018) · Nature Communications · doi:10.1038/s41467-018-03977-4 · Cited by 40
SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding sitePreprint
Zhang, L., Martini, G. D., Rube, H. T., Kribelbauer, J. F., Rastogi, C., FitzPatrick, V., Houtman, J. C. D., Bussemaker, H. J., & Pufall, M. A. (2017) · Genome Research · doi:10.1101/gr.222844.117 · Cited by 39
Self-citation
Adaptive Landscapes in the Age of Synthetic Biology
Yi, X., & Dean, A. M. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz004 · Cited by 38
DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
Barissi, S., Sala, A., Wieczór, M., Battistini, F., & Orozco, M. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac708 · Cited by 37
Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding
Zhao, J., Li, D., Seo, J., Allen, A. S., & Gordân, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56970-3_21 · Cited by 36
On the Use of Knowledge-Based Potentials for the Evaluation of Models of Protein–Protein, Protein–DNA, and Protein–RNA Interactions
Fornés, O., Garcı́a-Garcı́a, J., Bonet, J., & Oliva, B. (2014) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-800168-4.00004-4 · Cited by 35
Evolutionary potential of transcription factors for gene regulatory rewiring
Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., & Guet, C. C. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0651-y · Cited by 35
Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data
Levitsky, V. G., Kulakovskiy, I. V., Ershov, N., Oshchepkov, D. Y., Makeev, V. J., Hodgman, C. T., & Merkulova, T. I. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-80 · Cited by 35
Dynamic and Structural Modeling of the Specificity in Protein–DNA Interactions Guided by Binding Assay and Structure Data
Tan, C., & Takada, S. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00299 · Cited by 35
Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom
Abdullah‐Zawawi, M., Govender, N., Harun, S., Muhammad, N. A. N., Zainal, Z., & Mohamed‐Hussein, Z. (2022) · Plants · doi:10.3390/plants11192614 · Cited by 35
Exploring the complexity of MECP2 function in Rett syndrome
Liu, Y., Whitfield, T. W., Bell, G. W., Guo, R., Flamier, A., Young, R. A., & Jaenisch, R. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00926-1 · Cited by 39
A yeast one‐hybrid and microfluidics‐based pipeline to map mammalian gene regulatory networks
Gubelmann, C., Waszak, S. M., Isakova, A., Holcombe, W., Hens, K., Hafner, A., Feuz, J., Raghav, S. K., Simicevic, J., & Deplancke, B. (2013) · Molecular Systems Biology · doi:10.1038/msb.2013.38 · Cited by 33
Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE
Riley, T., Lazarovici, A., Mann, R. S., & Bussemaker, H. J. (2015) · eLife · doi:10.7554/elife.06397 · Cited by 34
Self-citation
Incomplete MyoD-induced transdifferentiation is associated with chromatin remodeling deficiencies
Manandhar, D., Song, L., Kabadi, A. M., Kwon, J. B., Edsall, L., Ehrlich, M., Tsumagari, K., Gersbach, C. A., Crawford, G. E., & Gordân, R. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx773 · Cited by 34
A survey on algorithms to characterize transcription factor binding sites
Tognon, M., Giugno, R., & Pinello, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad156 · Cited by 32
Direct AUC optimization of regulatory motifs
Zhu, L., Zhang, H., & Huang, D.-S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx255 · Cited by 32
Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome
Karimzadeh, M., & Hoffman, M. M. (2022) · Genome biology · doi:10.1186/s13059-022-02690-2 · Cited by 44
Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation
Wang, J., Malecka, A., Trøen, G., & Delabie, J. (2015) · BMC Genomics · doi:10.1186/1471-2164-16-s7-s12 · Cited by 31
Predicting tissue specific transcription factor binding sites
Zhong, S., He, X., & Bar‐Joseph, Z. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-796 · Cited by 30
An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning
Jing, F., Zhang, S.-W., Cao, Z., & Zhang, S. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2901789 · Cited by 31
Base-resolution prediction of transcription factor binding signals by a deep learning framework
Zhang, Q., He, Y., Wang, S., Chen, Z., Guo, Z., Cui, Z., Liu, Q., & Huang, D.-S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009941 · Cited by 33
Comparative chromatin accessibility upon BDNF stimulation delineates neuronal regulatory elements
Ibarra, I. L., Ratnu, V. S., Gordillo, L., Hwang, I., Mariani, L., Weinand, K., Hammarén, H. M., Heck, J., Bulyk, M. L., Savitski, M. M., et al. (2022) · Molecular Systems Biology · doi:10.15252/msb.202110473 · Cited by 33
Self-citation
Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual NetworksPreprint
Koo, P. K., Anand, P., Paul, S. B., & Eddy, S. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/418459 · Cited by 31
Decoding the non‐coding genome: elucidating genetic risk outside the coding genome
Barr, C. L., & Misener, V. L. (2015) · Genes Brain & Behavior · doi:10.1111/gbb.12269 · Cited by 29
DeepSELEX: inferring DNA-binding preferences from HT-SELEX data using multi-class CNNs
Asif, M., & Orenstein, Y. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa789 · Cited by 29
Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast
Tsai, Z. T., Shiu, S., & Tsai, H. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004418 · Cited by 29
DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding
Ma, W., Yang, L., Rohs, R., & Noble, W. S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx336 · Cited by 29
An RFX transcription factor regulates ciliogenesis in the closest living relatives of animals
Coyle, M. C., Tajima, A. M., Leon, F., Choksi, S. P., Yang, A. W. H., Espinoza, S., Hughes, T. R., Reiter, J. F., Booth, D. S., & King, N. (2023) · Current Biology · doi:10.1016/j.cub.2023.07.022 · Cited by 27
Self-citation
Motif models for RNA-binding proteins
Sasse, A., Laverty, K. U., Hughes, T. R., & Morris, Q. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.08.001 · Cited by 27
Self-citation
On the Value of Intra-Motif Dependencies of Human Insulator Protein CTCF
Eggeling, R., Gohr, A., Keilwagen, J., Mohr, M., Posch, S., Smith, A. D., & Große, I. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085629 · Cited by 26
Physicochemical models of protein–DNA binding with standard and modified base pairs
Chiu, T.-P., Rao, S., & Rohs, R. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205796120 · Cited by 28
SeAMotE: a method for high-throughput motif discovery in nucleic acid sequences
Agostini, F., Cirillo, D., Ponti, R. D., & Tartaglia, G. G. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-925 · Cited by 25
DiscMLA: An Efficient Discriminative Motif Learning Algorithm over High-Throughput Datasets
Zhang, H., Zhu, L., & Huang, D.-S. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2561930 · Cited by 25
Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility
Liu, S., Zibetti, C., Wan, J., Wang, G., Blackshaw, S., & Qian, J. X. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1769-7 · Cited by 25
Comparison of ChIP-Seq Data and a Reference Motif Set for Human KRAB C2H2 Zinc Finger Proteins
Barazandeh, M., Lambert, S. A., Albu, M., & Hughes, T. R. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.300296 · Cited by 27
Self-citation
An incoherent feedforward loop facilitates adaptive tuning of gene expression
Hong, J., Brandt, N., Abdul‐Rahman, F., Yang, A. W. H., Hughes, T., & Gresham, D. (2018) · eLife · doi:10.7554/elife.32323 · Cited by 24
Self-citation
Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptomePreprint
Karimzadeh, M., & Hoffman, M. M. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/168419 · Cited by 25
WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data
Zhang, H., Zhu, L., & Huang, D.-S. (2017) · Scientific Reports · doi:10.1038/s41598-017-03554-7 · Cited by 23
PthA4 AT , a 7.5‐repeats transcription activator‐like (TAL) effector from Xanthomonas citri ssp. citri , triggers citrus canker resistance
Roeschlin, R. A., Uviedo, F., García, L., Molina, M. C., Favaro, M. A., Chiesa, M. A., Tasselli, S., Franco‐Zorrilla, J. M., Forment, J., Gadea, J., et al. (2019) · Molecular Plant Pathology · doi:10.1111/mpp.12844 · Cited by 23
Constrained transcription factor spacing is prevalent and important for transcriptional control of mouse blood cells
Ng, F., Schütte, J., Ruau, D., Diamanti, E., Hannah, R., Kinston, S., & Göttgens, B. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1254 · Cited by 22
Distinct structural bases for sequence-specific DNA binding by mammalian BEN domain proteinsPreprint
Zheng, L., Liu, J., Niu, L., Kamran, M. Z., Yang, A. W. H., Jolma, A., Dai, Q., Hughes, T. R., Patel, D. J., Zhang, L., et al. (2022) · Genes & Development · doi:10.1101/gad.348993.121 · Cited by 23
Self-citation
MotifHyades: expectation maximization for de novo DNA motif pair discovery on paired sequences
Wong, K. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx381 · Cited by 22
DNA Motif Databases and Their Uses
Stormo, G. D. (2015) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0215s51 · Cited by 22
Inherent limitations of probabilistic models for protein-DNA binding specificity
Ruan, S., & Stormo, G. D. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005638 · Cited by 22
Essential guidelines for computational method benchmarking
Weber, L. M., Saelens, W., Cannoodt, R., Soneson, C., Hapfelmeier, A., Gardner, P. P., Boulesteix, A.-L., Saeys, Y., & Robinson, M. D. (2019) · Genome Biology · doi:10.1186/s13059-019-1738-8 · Cited by 21
Transcription Factor Binding Affinities and DNA Shape Readout
Schnepf, M., von Reutern, M., Ludwig, C., Jung, C., & Gaul, U. (2020) · iScience · doi:10.1016/j.isci.2020.101694 · Cited by 21
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 International Edition · doi:10.1002/anie.202310913 · Cited by 21
Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning
Gao, Z., & Ruan, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx115 · Cited by 21
Discriminative motif optimization based on perceptron training
Patel, R. Y., & Stormo, G. D. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt748 · Cited by 20
Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models
Isakova, A., Berset, Y., Vassily Hatzimanikatis, A., & Deplancke, B. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.691154 · Cited by 23
SELMAP - SELEX affinity landscape MAPping of transcription factor binding sites using integrated microfluidics
Chen, D., Orenstein, Y., Golodnitsky, R., Pellach, M., Avrahami, D., Wachtel, C., Ovadia-Shochat, A., Shir-Shapira, H., Kedmi, A., Juven‐Gershon, T., et al. (2016) · Scientific Reports · doi:10.1038/srep33351 · Cited by 20
A systematic, large-scale comparison of transcription factor binding site models
Hombach, D., Schwarz, J. M., Robinson, P. N., Schuelke, M., & Seelow, D. (2016) · BMC Genomics · doi:10.1186/s12864-016-2729-8 · Cited by 20
Allele-specific transcription factor binding as a benchmark for assessing variant impact predictorsPreprint
Wagih, O., Merico, D., Delong, A., & Frey, B. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/253427 · Cited by 19
Genetics of juvenile idiopathic arthritis
Moncrieffe, H., Prahalad, S., & Thompson, S. D. (2014) · Current Opinion in Rheumatology · doi:10.1097/bor.0000000000000094 · Cited by 26
RNA-centric approaches to study RNA-protein interactions in vitro and in silico
Dasti, A., Cid-Samper, F., Bechara, E., & Tartaglia, G. G. (2019) · Methods · doi:10.1016/j.ymeth.2019.09.011 · Cited by 19
Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability
van de Geijn, B., Finucane, H. K., Gazal, S., Hormozdiari, F., Amariuta, T., Liu, X., Gusev, A., Loh, P., Reshef, Y., Kichaev, G., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz226 · Cited by 18
cisExpress: motif detection in DNA sequences
Tříska, M., Grocutt, D., Southern, J., Murphy, D. J., & Tatarinova, T. V. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt366 · Cited by 18
Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites
Zhang, Q., Yu, W., Han, K., Nandi, A. K., & Huang, D.-S. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3025579 · Cited by 18
TherMos: Estimating protein–DNA binding energies from in vivo binding profiles
Sun, W., Hu, T. X., Lim, M. H. K., Ng, C. K. L., Choo, S. H., Castro, D. S., Drechsel, D., Guillemot, F., Kolatkar, P. R., Jauch, R., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt250 · Cited by 18
Modeling protein–DNA binding via high-throughput in vitro technologies
Orenstein, Y., & Ron Shamir, A. (2016) · Briefings in Functional Genomics · doi:10.1093/bfgp/elw030 · Cited by 19
Neurospora crassa developmental control mediated by the FLB-3 transcription factor
Boni, A. C., Ambrósio, D. L., Cupertino, F. B., Montenegro‐Montero, A., Virgílio, S., Freitas, F. Z., Corrocher, F. A., Gonçalves, R. D., Yang, A. W. H., Weirauch, M. T., et al. (2018) · Fungal Biology · doi:10.1016/j.funbio.2018.01.004 · Cited by 19
Self-citation
Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif
Ali, M. Z., Parisutham, V., Choubey, S., & Brewster, R. C. (2020) · eLife · doi:10.7554/elife.56517 · Cited by 16
Establishing the Architecture of Plant Gene Regulatory Networks
Yang, F., Ouma, W. Z., Li, W., Doseff, A. I., & Grotewold, E. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.03.003 · Cited by 18
On the dependent recognition of some long zinc finger proteins
Zuo, Z., Billings, T., Walker, M., Petkov, P. M., Fordyce, P. M., & Stormo, G. D. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad207 · Cited by 21
Quantitative modeling of gene expression using DNA shape features of binding sites
Peng, P.-C., & Sinha, S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw446 · Cited by 17
Targeting and silencing of rhodopsin by ectopic expression of the transcription factor KLF15
Botta, S., de Prisco, N., Marrocco, E., Renda, M., Sofia, M., Curion, F., Bacci, M. L., Ventrella, D., Wilson, C., Gesualdo, C., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.96560 · Cited by 18
The Role of Chromatin Accessibility in cis-Regulatory Evolution
Peng, P.-C., Khoueiry, P., Girardot, C., Reddington, J. P., Garfield, D. A., Furlong, E. E. M., & Sinha, S. (2019) · Genome Biology and Evolution · doi:10.1093/gbe/evz103 · Cited by 19
Crystal structure of the BRPF2 PWWP domain in complex with DNA reveals a different binding mode than the HDGF family of PWWP domains
Zhang, M., Lei, M., Su, Q., Dong, A., Yang, A. W. H., Li, Y., Loppnau, P., Hughes, T. R., Min, J., & Liu, Y. (2021) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2021.194688 · Cited by 17
Self-citation
A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data
Polishchuk, M. S., Paz, I., Kohen, R., Mesika, R., Yakhini, Z., & Mandel‐Gutfreund, Y. (2017) · Methods · doi:10.1016/j.ymeth.2017.03.003 · Cited by 17
Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity
Zandvakili, A., Campbell, I., Gutzwiller, L. M., Weirauch, M. T., & Gebelein, B. A. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007289 · Cited by 17
Self-citation
Specificity landscapes unmask submaximal binding site preferences of transcription factors
Bhimsaria, D., Rodríguez‐Martínez, J. A., Pan, J., Roston, D., Korkmaz, E. N., Cui, Q., Ramanathan, P., & Ansari, A. Z. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811431115 · Cited by 17
DNA Sequence Motif
Kulakovskiy, I. V., & Makeev, V. J. (2013) · Advances in protein chemistry and structural biology · doi:10.1016/b978-0-12-411637-5.00005-6 · Cited by 17
DREAMTools: a Python package for scoring collaborative challenges
Cokelaer, T., Bansal, M., Bare, C., Bilal, E., Bot, B. M., Neto, E. C., Eduati, F., de la Fuente, A., Gönen, M., Hill, S. M., et al. (2016) · F1000Research · doi:10.12688/f1000research.7118.2 · Cited by 17
Self-citation
Intracellular and Intercellular Gene Regulatory Network Inference From Time-Course Individual RNA-Seq
Kashima, M., Shida, Y., Yamashiro, T., Hirata, H., & Kurosaka, H. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.777299 · Cited by 17
RAP: Accurate and Fast Motif Finding Based on Protein-Binding Microarray Data
Orenstein, Y., Mick, E., & Ron Shamir, A. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2012.0253 · Cited by 16
Predicting conformational ensembles and genome-wide transcription factor binding sites from DNA sequences
Andrabi, M., Hutchins, A. P., Miranda‐Saavedra, D., Kono, H., Nussinov, R., Mizuguchi, K., & Ahmad, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-03199-6 · Cited by 16
DREAMTools: a Python package for scoring collaborative challenges
Cokelaer, T., Bansal, M., Bare, C., Bilal, E., Bot, B. M., Neto, E. C., Eduati, F., Gönen, M., Hill, S. M., Hoff, B., et al. (2015) · F1000Research · doi:10.12688/f1000research.7118.1 · Cited by 17
Self-citation
Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer
Tříska, M., Ivliev, A. E., Nikolsky, Y., & Tatarinova, T. V. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7027-8_11 · Cited by 15
Periodontitis Risk Variants at SIGLEC5 Impair ERG and MAFB Binding
Mueller, R., Chopra, A., Dommisch, H., & Schäefer, A. S. (2021) · Journal of Dental Research · doi:10.1177/00220345211049984 · Cited by 15
Comparison of discriminative motif optimization using matrix and DNA shape-based models
Ruan, S., & Stormo, G. D. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2104-7 · Cited by 15
DNA sequence classification based on MLP with PILAE algorithm
Mahmoud, M. A. B., & Guo, P. (2020) · Soft Computing · doi:10.1007/s00500-020-05429-y · Cited by 14

Authors: DREAM5 Consortium, Weirauch, M. T., Cote, A., Norel, R., Annala, M., Zhao, Y., Riley, T. R., Saez-Rodriguez, J., Cokelaer, T., Vedenko, A., Talukder, S., Bussemaker, H. J., Morris, Q. D., Bulyk, M. L., Stolovitzky, G., & Hughes, T. R.
Affiliations: University of Toronto (CA)
IBM (United States) (US)
Tampere University of Technology (FI)
University of Pennsylvania (US)
Columbia University Irving Medical Center (US)
European Bioinformatics Institute (GB)
Brigham and Women's Hospital (US)
Corresponding author: Hughes, Timothy R
ORCID: Weirauch, Matthew T
Norel, Raquel
Annala, Matti
Zhao, Yue
Cokelaer, Thomas
Bussemaker, Harmen J
Morris, Quaid D
Bulyk, Martha L
Stolovitzky, Gustavo
Hughes, Timothy R
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2013-01-27
Volume: 31
Issue: 2
Pages: 126-134
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/nbt.2486
PMC: PMC3687085
OpenAlex: W2159324718
Cited by: 381
References: 59
Citations by year: 2026: 10 · 2025: 23 · 2024: 18 · 2023: 25 · 2022: 18 · 2021: 26 · 2020: 23 · 2019: 34 · 2018: 32 · 2017: 44 · 2016: 36 · 2015: 40 · 2014: 31 · 2013: 21
FWCI: 19.10
Type: journal article
Language: en
Open access: green
europepmc.org
Licence: www.springer.com/tdm (tdm)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
National University of Singapore (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
Funders: National Science Foundation (R01HG003008, 0957573, 1022140)
March of Dimes Foundation
John Simon Guggenheim Memorial Foundation
Canadian Institute for Advanced Research
Genome Canada
Ontario Genomics
Ontario Genomics Institute
European Commission (223575)
Academy of Finland (260403)
Israel Science Foundation (802/08)
Tel Aviv University
National Institutes of Health (U54-CA121852)
Canadian Institutes of Health Research (MOP-77721)
Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory
National Human Genome Research Institute (R01HG003985, R01HG003008)
National Cancer Institute (U54CA121852)
National Institute on Minority Health and Health Disparities (G12MD007591)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords: transcription factor binding sites, DNA binding specificity, position weight matrices, protein binding microarrays, information content
MeSH terms: Algorithms, Animals, DNA-Binding Proteins, Transcription Factors, Genome, Computational Biology, Protein Array Analysis, Mice, Position-Specific Scoring Matrices, Nucleotide Motifs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Evaluation of methods for modeling transcription factor sequence specificity

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks