Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2529
- Type
- D - Journal article
- DOI
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10.1073/pnas.1420404112
- Title of journal
- Proceedings of the National Academy of Sciences
- Article number
- -
- First page
- 13115
- Volume
- 112
- Issue
- 42
- ISSN
- 0027-8424
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
9
- Research group(s)
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C - Machine Learning
- Citation count
- 46
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper is a collaboration of computational researchers and biologists from four countries, and eight different institutions. Transcriptional delays are difficult to measure but important in understanding biological function. The paper uses machine learning to integrate ChIP-Seq data (about transcription factor binding), RNA-Seq data (about gene expression) and a mechanistic model of transcription to infer transcription delay. It emerged from the FP7 ERASysBio+ European Research Network. Papers that cite this work come from diverse domains: the geosciences, biology, nonlinear dynamics, and Bayesian analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -