An Integrative Approach to Predicting the Functional Effects of Non-Coding and Coding Sequence Variation
- Submitting institution
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University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 163359665
- Type
- D - Journal article
- DOI
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10.1093/bioinformatics/btv009
- Title of journal
- Bioinformatics
- Article number
- -
- First page
- 1536
- Volume
- 31
- Issue
- 10
- ISSN
- 1367-4803
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- 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
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7
- Research group(s)
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A - Artificial Intelligence and Autonomy
- Citation count
- 230
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This presents one of the best performing predictors of the functionality of germline mutations in non-coding parts of the human genome. The corresponding tool (FATHMM-MKL) was determined as state-of-art in independent studies (Journal of Medical Genetics, 54:134-144 (2017)). It is an embedded tool at well-known databases e.g. as an annotator at the COSMIC cancer database (https://cancer.sanger.ac.uk/cosmic/analyses). FATHMM-MKL is now an established method for predicting which single point mutations in the human genome are disease-drivers and which are not. Research was funded by EPSRC (PI: Campbell EP/M01715X/1, Novel Methodology for predicting the Functional Effects of Genetic Variation).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -