Title |
Computational approaches to interpreting genomic sequence variation
|
---|---|
Published in |
Genome Medicine, October 2014
|
DOI | 10.1186/s13073-014-0087-1 |
Pubmed ID | |
Authors |
Graham RS Ritchie, Paul Flicek |
Abstract |
Identifying sequence variants that play a mechanistic role in human disease and other phenotypes is a fundamental goal in human genetics and will be important in translating the results of variation studies. Experimental validation to confirm that a variant causes the biochemical changes responsible for a given disease or phenotype is considered the gold standard, but this cannot currently be applied to the 3 million or so variants expected in an individual genome. This has prompted the development of a wide variety of computational approaches that use several different sources of information to identify functional variation. Here, we review and assess the limitations of computational techniques for categorizing variants according to functional classes, prioritizing variants for experimental follow-up and generating hypotheses about the possible molecular mechanisms to inform downstream experiments. We discuss the main current bioinformatics approaches to identifying functional variation, including widely used algorithms for coding variation such as SIFT and PolyPhen and also novel techniques for interpreting variation across the genome. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 25% |
United Kingdom | 7 | 25% |
Spain | 3 | 11% |
Australia | 1 | 4% |
Japan | 1 | 4% |
Switzerland | 1 | 4% |
France | 1 | 4% |
Turkey | 1 | 4% |
Unknown | 6 | 21% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 19 | 68% |
Members of the public | 8 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 1% |
Spain | 2 | 1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Russia | 1 | <1% |
Germany | 1 | <1% |
Bosnia and Herzegovina | 1 | <1% |
United States | 1 | <1% |
Unknown | 130 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 37 | 26% |
Student > Ph. D. Student | 33 | 24% |
Student > Postgraduate | 13 | 9% |
Student > Master | 12 | 9% |
Student > Bachelor | 9 | 6% |
Other | 24 | 17% |
Unknown | 12 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 47 | 34% |
Biochemistry, Genetics and Molecular Biology | 38 | 27% |
Medicine and Dentistry | 13 | 9% |
Computer Science | 10 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 2% |
Other | 11 | 8% |
Unknown | 18 | 13% |