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Romero R, Menichelli C, Vroland C, Marin JM, Lèbre S, Lecellier CH, Bréhélin L. TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors. Genome Biol 2024; 25:187. [PMID: 38987807 DOI: 10.1186/s13059-024-03321-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 06/24/2024] [Indexed: 07/12/2024] Open
Abstract
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence features explaining the binding differences observed between two ChIP-seq experiments targeting either the same TF in two conditions or two TFs with similar motifs (paralogous TFs). TFscope systematically investigates differences in the core motif, nucleotide environment and co-factor motifs, and provides the contribution of each key feature in the two experiments. TFscope was applied to > 305 ChIP-seq pairs, and several examples are discussed.
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Affiliation(s)
- Raphaël Romero
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
- IMAG, Univ Montpellier, CNRS, Montpellier, France
| | | | - Christophe Vroland
- LIRMM, Univ Montpellier, CNRS, Montpellier, France
- Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France
| | | | - Sophie Lèbre
- IMAG, Univ Montpellier, CNRS, Montpellier, France.
- AMIS, Université Paul-Valéry-Montpellier 3, Montpellier, France.
| | - Charles-Henri Lecellier
- LIRMM, Univ Montpellier, CNRS, Montpellier, France.
- Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France.
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2
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Lipps G. Definition of the binding specificity of the T7 bacteriophage primase by analysis of a protein binding microarray using a thermodynamic model. Nucleic Acids Res 2024; 52:4818-4829. [PMID: 38597656 PMCID: PMC11109968 DOI: 10.1093/nar/gkae215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/26/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
Protein binding microarrays (PBM), SELEX, RNAcompete and chromatin-immunoprecipitation have been intensively used to determine the specificity of nucleic acid binding proteins. While the specificity of proteins with pronounced sequence specificity is straightforward, the determination of the sequence specificity of proteins of modest sequence specificity is more difficult. In this work, an explorative data analysis workflow for nucleic acid binding data was developed that can be used by scientists that want to analyse their binding data. The workflow is based on a regressor realized in scikit-learn, the major machine learning module for the scripting language Python. The regressor is built on a thermodynamic model of nucleic acid binding and describes the sequence specificity with base- and position-specific energies. The regressor was used to determine the binding specificity of the T7 primase. For this, we reanalysed the binding data of the T7 primase obtained with a custom PBM. The binding specificity of the T7 primase agrees with the priming specificity (5'-GTC) and the template (5'-GGGTC) for the preferentially synthesized tetraribonucleotide primer (5'-pppACCC) but is more relaxed. The dominant contribution of two positions in the motif can be explained by the involvement of the initiating and elongating nucleotides for template binding.
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Affiliation(s)
- Georg Lipps
- Institute of Chemistry and Bioanalytics, University of Applied Sciences Northwestern Switzerland, 4132 Muttenz, Switzerland
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3
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Chen N, Yu J, Liu Z, Meng L, Li X, Wong KC. Discovering DNA shape motifs with multiple DNA shape features: generalization, methods, and validation. Nucleic Acids Res 2024; 52:4137-4150. [PMID: 38572749 PMCID: PMC11077088 DOI: 10.1093/nar/gkae210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/05/2024] Open
Abstract
DNA motifs are crucial patterns in gene regulation. DNA-binding proteins (DBPs), including transcription factors, can bind to specific DNA motifs to regulate gene expression and other cellular activities. Past studies suggest that DNA shape features could be subtly involved in DNA-DBP interactions. Therefore, the shape motif annotations based on intrinsic DNA topology can deepen the understanding of DNA-DBP binding. Nevertheless, high-throughput tools for DNA shape motif discovery that incorporate multiple features altogether remain insufficient. To address it, we propose a series of methods to discover non-redundant DNA shape motifs with the generalization to multiple motifs in multiple shape features. Specifically, an existing Gibbs sampling method is generalized to multiple DNA motif discovery with multiple shape features. Meanwhile, an expectation-maximization (EM) method and a hybrid method coupling EM with Gibbs sampling are proposed and developed with promising performance, convergence capability, and efficiency. The discovered DNA shape motif instances reveal insights into low-signal ChIP-seq peak summits, complementing the existing sequence motif discovery works. Additionally, our modelling captures the potential interplays across multiple DNA shape features. We provide a valuable platform of tools for DNA shape motif discovery. An R package is built for open accessibility and long-lasting impact: https://zenodo.org/doi/10.5281/zenodo.10558980.
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Affiliation(s)
- Nanjun Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Zhe Liu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Lingkuan Meng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Changchun City, Jilin Province, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
- Hong Kong Institute of Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
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4
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Cooper BH, Dantas Machado AC, Gan Y, Aparicio O, Rohs R. DNA binding specificity of all four Saccharomyces cerevisiae forkhead transcription factors. Nucleic Acids Res 2023; 51:5621-5633. [PMID: 37177995 PMCID: PMC10287902 DOI: 10.1093/nar/gkad372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 04/19/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Quantifying the nucleotide preferences of DNA binding proteins is essential to understanding how transcription factors (TFs) interact with their targets in the genome. High-throughput in vitro binding assays have been used to identify the inherent DNA binding preferences of TFs in a controlled environment isolated from confounding factors such as genome accessibility, DNA methylation, and TF binding cooperativity. Unfortunately, many of the most common approaches for measuring binding preferences are not sensitive enough for the study of moderate-to-low affinity binding sites, and are unable to detect small-scale differences between closely related homologs. The Forkhead box (FOX) family of TFs is known to play a crucial role in regulating a variety of key processes from proliferation and development to tumor suppression and aging. By using the high-sequencing depth SELEX-seq approach to study all four FOX homologs in Saccharomyces cerevisiae, we have been able to precisely quantify the contribution and importance of nucleotide positions all along an extended binding site. Essential to this process was the alignment of our SELEX-seq reads to a set of candidate core sequences determined using a recently developed tool for the alignment of enriched k-mers and a newly developed approach for the reprioritization of candidate cores.
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Affiliation(s)
- Brendon H Cooper
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Ana Carolina Dantas Machado
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Yan Gan
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - Oscar M Aparicio
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Remo Rohs
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
- Departments of Chemistry, Physics & Astronomy, and Computer Science, University of Southern California, Los Angeles, CA 90089, USA
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Li M, Yao T, Lin W, Hinckley WE, Galli M, Muchero W, Gallavotti A, Chen JG, Huang SSC. Double DAP-seq uncovered synergistic DNA binding of interacting bZIP transcription factors. Nat Commun 2023; 14:2600. [PMID: 37147307 PMCID: PMC10163045 DOI: 10.1038/s41467-023-38096-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/15/2023] [Indexed: 05/07/2023] Open
Abstract
Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. Dimerization of BASIC LEUCINE ZIPPER (bZIP) TFs are critical for their functions, but the molecular mechanism underlying the DNA binding and functional specificity of homo- versus heterodimers remains elusive. To address this gap, we present the double DNA Affinity Purification-sequencing (dDAP-seq) technique that maps heterodimer binding sites on endogenous genomic DNA. Using dDAP-seq we profile twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis and show that heterodimerization significantly expands the DNA binding preferences of these TFs. Analysis of dDAP-seq binding sites reveals the function of bZIP9 in abscisic acid response and the role of bZIP53 heterodimer-specific binding in seed maturation. The C/S1 heterodimers show distinct preferences for the ACGT elements recognized by plant bZIPs and motifs resembling the yeast GCN4 cis-elements. This study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.
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Affiliation(s)
- Miaomiao Li
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Tao Yao
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Wanru Lin
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Will E Hinckley
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA
| | - Mary Galli
- Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, 08854-8020, USA
| | - Wellington Muchero
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Andrea Gallavotti
- Waksman Institute of Microbiology, Rutgers University, Piscataway, NJ, 08854-8020, USA
| | - Jin-Gui Chen
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA
| | - Shao-Shan Carol Huang
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY, 10003, USA.
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Lebeau B, Zhao K, Jangal M, Zhao T, Guerra M, Greenwood CMT, Witcher M. Single base-pair resolution analysis of DNA binding motif with MoMotif reveals an oncogenic function of CTCF zinc-finger 1 mutation. Nucleic Acids Res 2022; 50:8441-8458. [PMID: 35947648 PMCID: PMC9410893 DOI: 10.1093/nar/gkac658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/21/2022] [Indexed: 12/24/2022] Open
Abstract
Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts.
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Affiliation(s)
| | | | - Maika Jangal
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Tiejun Zhao
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Maria Guerra
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Celia M T Greenwood
- Correspondence may also be addressed to Celia Greenwood. Tel: +1 514 340 8222 (Ext 28397);
| | - Michael Witcher
- To whom correspondence should be addressed. Tel: +1 514 340 8222 (Ext 23363);
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7
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Puig RR, Boddie P, Khan A, Castro-Mondragon JA, Mathelier A. UniBind: maps of high-confidence direct TF-DNA interactions across nine species. BMC Genomics 2021; 22:482. [PMID: 34174819 PMCID: PMC8236138 DOI: 10.1186/s12864-021-07760-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 05/27/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq). RESULTS In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface ( https://unibind.uio.no/ ), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. CONCLUSIONS UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.
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Affiliation(s)
- Rafael Riudavets Puig
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349, Oslo, Norway
| | - Paul Boddie
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349, Oslo, Norway
| | - Aziz Khan
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349, Oslo, Norway
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0349, Oslo, Norway.
- Department of Medical Genetics, Oslo University Hospital, Oslo, 0424, Norway.
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8
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Wetzel JL, Singh M. Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination. Nucleic Acids Res 2020; 48:e9. [PMID: 31777934 PMCID: PMC7028011 DOI: 10.1093/nar/gkz1087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 10/03/2019] [Accepted: 11/01/2019] [Indexed: 01/31/2023] Open
Abstract
We are now in an era where protein-DNA interactions have been experimentally assayed for thousands of DNA-binding proteins. In order to infer DNA-binding specificities from these data, numerous sophisticated computational methods have been developed. These approaches typically infer DNA-binding specificities by considering interactions for each protein independently, ignoring related and potentially valuable interaction information across other proteins that bind DNA via the same structural domain. Here we introduce a framework for inferring DNA-binding specificities by considering protein-DNA interactions for entire groups of structurally similar proteins simultaneously. We devise both constrained optimization and label propagation algorithms for this task, each balancing observations at the individual protein level against dataset-wide consistency of interaction preferences. We test our approaches on two large, independent Cys2His2 zinc finger protein-DNA interaction datasets. We demonstrate that jointly inferring specificities within each dataset individually dramatically improves accuracy, leading to increased agreement both between these two datasets and with a fixed external standard. Overall, our results suggest that sharing protein-DNA interaction information across structurally similar proteins is a powerful means to enable accurate inference of DNA-binding specificities.
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Affiliation(s)
- Joshua L Wetzel
- The Lewis-Sigler Institute for Integrative Genomics, Princeton, NJ 08544, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Mona Singh
- The Lewis-Sigler Institute for Integrative Genomics, Princeton, NJ 08544, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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Pal S, Hoinka J, Przytycka TM. Co-SELECT reveals sequence non-specific contribution of DNA shape to transcription factor binding in vitro. Nucleic Acids Res 2020; 47:6632-6641. [PMID: 31226207 DOI: 10.1093/nar/gkz540] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 05/31/2019] [Accepted: 06/06/2019] [Indexed: 12/22/2022] Open
Abstract
Understanding the principles of DNA binding by transcription factors (TFs) is of primary importance for studying gene regulation. Recently, several lines of evidence suggested that both DNA sequence and shape contribute to TF binding. However, the following compelling question is yet to be considered: in the absence of any sequence similarity to the binding motif, can DNA shape still increase binding probability? To address this challenge, we developed Co-SELECT, a computational approach to analyze the results of in vitro HT-SELEX experiments for TF-DNA binding. Specifically, Co-SELECT leverages the presence of motif-free sequences in late HT-SELEX rounds and their enrichment in weak binders allows Co-SELECT to detect an evidence for the role of DNA shape features in TF binding. Our approach revealed that, even in the absence of the sequence motif, TFs have propensity to bind to DNA molecules of the shape consistent with the motif specific binding. This provides the first direct evidence that shape features that accompany the preferred sequence motifs also bestow an advantage for weak, sequence non-specific binding.
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Affiliation(s)
- Soumitra Pal
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Jan Hoinka
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
| | - Teresa M Przytycka
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA
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