Seven myths of how transcription factors read the cis-regulatory code Article Swipe
Genomics data are now being generated at large quantities, of exquisite high resolution and from single cells. They offer a unique opportunity to develop powerful machine learning algorithms, including neural networks, to uncover the rules of the cis-regulatory code. However, current modeling assumptions are often not based on state-of-the-art knowledge of the cis-regulatory code from transcription, developmental genetics, imaging and structural studies. Here I aim to fill this gap by giving a brief historical overview of the field, describing common misconceptions and providing knowledge that might help to guide computational approaches. I will describe the principles and mechanisms involved in the combinatorial requirement of transcription factor binding motifs for enhancer activity, including the role of chromatin accessibility, repressors and low-affinity motifs in the cis-regulatory code. Deciphering the cis-regulatory code would unlock an enormous amount of regulatory information in the genome and would allow us to locate cis-regulatory genetic variants involved in development and disease.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.coisb.2020.08.002
- OA Status
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- Cited By
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W3083240949Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.coisb.2020.08.002Digital Object Identifier
- Title
-
Seven myths of how transcription factors read the cis-regulatory codeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-09-04Full publication date if available
- Authors
-
Julia ZeitlingerList of authors in order
- Landing page
-
https://doi.org/10.1016/j.coisb.2020.08.002Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.coisb.2020.08.002Direct OA link when available
- Concepts
-
Enhancer, Computational biology, Chromatin, Computer science, Transcription factor, Biology, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
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114Total citation count in OpenAlex
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2025: 20, 2024: 28, 2023: 30, 2022: 19, 2021: 17Per-year citation counts (last 5 years)
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89Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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