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Raditsa V, Tsukanov A, Bogomolov A, Levitsky V. Genomic background sequences systematically outperform synthetic ones in de novo motif discovery for ChIP-seq data. NAR Genom Bioinform 2024; 6:lqae090. [PMID: 39071850 PMCID: PMC11282361 DOI: 10.1093/nargab/lqae090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/03/2024] [Accepted: 07/19/2024] [Indexed: 07/30/2024] Open
Abstract
Efficient de novo motif discovery from the results of wide-genome mapping of transcription factor binding sites (ChIP-seq) is dependent on the choice of background nucleotide sequences. The foreground sequences (ChIP-seq peaks) represent not only specific motifs of target transcription factors, but also the motifs overrepresented throughout the genome, such as simple sequence repeats. We performed a massive comparison of the 'synthetic' and 'genomic' approaches to generate background sequences for de novo motif discovery. The 'synthetic' approach shuffled nucleotides in peaks, while in the 'genomic' approach selected sequences from the reference genome randomly or only from gene promoters according to the fraction of A/T nucleotides in each sequence. We compiled the benchmark collections of ChIP-seq datasets for mouse, human and Arabidopsis, and performed de novo motif discovery. We showed that the genomic approach has both more robust detection of the known motifs of target transcription factors and more stringent exclusion of the simple sequence repeats as possible non-specific motifs. The advantage of the genomic approach over the synthetic approach was greater in plants compared to mammals. We developed the AntiNoise web service (https://denovosea.icgbio.ru/antinoise/) that implements a genomic approach to extract genomic background sequences for twelve eukaryotic genomes.
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Affiliation(s)
- Vladimir V Raditsa
- Department of System Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Anton V Tsukanov
- Department of System Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Anton G Bogomolov
- Department of Cell Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
| | - Victor G Levitsky
- Department of System Biology, Institute of Cytology and Genetics, Novosibirsk 630090, Russia
- Department of Natural Science, Novosibirsk State University, Novosibirsk 630090, Russia
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Viner C, Ishak CA, Johnson J, Walker NJ, Shi H, Sjöberg-Herrera MK, Shen SY, Lardo SM, Adams DJ, Ferguson-Smith AC, De Carvalho DD, Hainer SJ, Bailey TL, Hoffman MM. Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet. Genome Biol 2024; 25:11. [PMID: 38191487 PMCID: PMC10773111 DOI: 10.1186/s13059-023-03070-0] [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: 03/08/2023] [Accepted: 09/21/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Transcription factors bind DNA in specific sequence contexts. In addition to distinguishing one nucleobase from another, some transcription factors can distinguish between unmodified and modified bases. Current models of transcription factor binding tend not to take DNA modifications into account, while the recent few that do often have limitations. This makes a comprehensive and accurate profiling of transcription factor affinities difficult. RESULTS Here, we develop methods to identify transcription factor binding sites in modified DNA. Our models expand the standard A/C/G/T DNA alphabet to include cytosine modifications. We develop Cytomod to create modified genomic sequences and we also enhance the MEME Suite, adding the capacity to handle custom alphabets. We adapt the well-established position weight matrix (PWM) model of transcription factor binding affinity to this expanded DNA alphabet. Using these methods, we identify modification-sensitive transcription factor binding motifs. We confirm established binding preferences, such as the preference of ZFP57 and C/EBPβ for methylated motifs and the preference of c-Myc for unmethylated E-box motifs. CONCLUSIONS Using known binding preferences to tune model parameters, we discover novel modified motifs for a wide array of transcription factors. Finally, we validate our binding preference predictions for OCT4 using cleavage under targets and release using nuclease (CUT&RUN) experiments across conventional, methylation-, and hydroxymethylation-enriched sequences. Our approach readily extends to other DNA modifications. As more genome-wide single-base resolution modification data becomes available, we expect that our method will yield insights into altered transcription factor binding affinities across many different modifications.
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Affiliation(s)
- Coby Viner
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Charles A Ishak
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Epigenetics and Molecular Carcinogenesis, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - James Johnson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Nicolas J Walker
- Department of Genetics, University of Cambridge, Cambridge, England
| | - Hui Shi
- Department of Genetics, University of Cambridge, Cambridge, England
| | - Marcela K Sjöberg-Herrera
- Wellcome Sanger Institute, Cambridge, England
- Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Shu Yi Shen
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Santana M Lardo
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Daniel D De Carvalho
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sarah J Hainer
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy L Bailey
- Department of Pharmacology, University of Nevada, Reno, Reno, NV, USA
| | - Michael M Hoffman
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
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Samee MAH. Noncanonical binding of transcription factors: time to revisit specificity? Mol Biol Cell 2023; 34:pe4. [PMID: 37486893 PMCID: PMC10398899 DOI: 10.1091/mbc.e22-08-0325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 06/05/2023] [Accepted: 06/21/2023] [Indexed: 07/26/2023] Open
Abstract
Transcription factors (TFs) are one of the most studied classes of DNA-binding proteins that have a direct functional impact on gene transcription and thus, on human physiology and disease. The mechanisms that TFs use for recognizing target DNA binding sites have been studied for nearly five decades, yet they remain poorly understood. It is classically assumed that a TF recognizes a specific sequence pattern, or motif, as its binding sites. However, recent studies are consistently finding examples of noncanonical binding, that is, TFs binding at sites that do not resemble their sequence motifs. Here we review the current literature on four major types of noncanonical TF binding, namely binding based on DNA shape readout, at Guanine-quadruplex structures, at repeat sequences, and bispecific binding. These examples point to a critical need for studies to unify our current observations, many of which are at odds with the "one TF, one motif" view, into a more comprehensive definition of the DNA-binding specificity of TFs.
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