Base-resolution models of transcription-factor binding reveal soft motif syntax
The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluri...
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| Veröffentlicht in: | Nature genetics 2021-03, Vol.53 (3), p.354-366 |
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| creator | Avsec, Žiga Weilert, Melanie Shrikumar, Avanti Krueger, Sabrina Alexandari, Amr Dalal, Khyati Fropf, Robin McAnany, Charles Gagneur, Julien Kundaje, Anshul Zeitlinger, Julia |
| description | The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding. |
| doi_str_mv | 10.1038/s41588-021-00782-6 |
| format | Article |
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BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding.</description><subject>45/100</subject><subject>45/15</subject><subject>45/23</subject><subject>45/70</subject><subject>631/114</subject><subject>631/1647/2217/2088</subject><subject>631/208/212</subject><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Animals</subject><subject>Binding</subject><subject>Binding Sites</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cancer Research</subject><subject>Chromatin</subject><subject>Chromatin Immunoprecipitation</subject><subject>Clustered Regularly Interspaced Short Palindromic Repeats</subject><subject>Computational Biology - methods</subject><subject>CRISPR</subject><subject>Deep Learning</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>Experiments</subject><subject>Gene Function</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Human Genetics</subject><subject>Immunoprecipitation</subject><subject>Mice</subject><subject>Mouse Embryonic Stem Cells - 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We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)–nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
BPNet is an interpretable deep learning tool that predicts transcription-factor binding profiles from DNA sequence at base-pair resolution, enabling the identification of motifs and the regulatory syntax underlying transcription-factor binding.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>33603233</pmid><doi>10.1038/s41588-021-00782-6</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-7790-8936</orcidid><orcidid>https://orcid.org/0000-0003-3084-2287</orcidid><orcidid>https://orcid.org/0000-0002-8924-8365</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 45/100 45/15 45/23 45/70 631/114 631/1647/2217/2088 631/208/212 Agriculture Animal Genetics and Genomics Animals Binding Binding Sites Biomedical and Life Sciences Biomedicine Cancer Research Chromatin Chromatin Immunoprecipitation Clustered Regularly Interspaced Short Palindromic Repeats Computational Biology - methods CRISPR Deep Learning Deoxyribonucleic acid DNA Experiments Gene Function Genomes Genomics Human Genetics Immunoprecipitation Mice Mouse Embryonic Stem Cells - physiology Mutation Nanog Homeobox Protein - metabolism Neural networks Neural Networks, Computer Nucleotide Motifs Nucleotide sequence Octamer Transcription Factor-3 - metabolism Periodicity Pluripotency Proteins Regulatory sequences Reproducibility of Results SOXB1 Transcription Factors - metabolism Stem cells Syntax Transcription Factors - metabolism |
| title | Base-resolution models of transcription-factor binding reveal soft motif syntax |
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