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Computational approaches to interpreting genomic sequence variation
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254438/ https://www.ncbi.nlm.nih.gov/pubmed/25473426 http://dx.doi.org/10.1186/s13073-014-0087-1 |
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author | Ritchie, Graham RS Flicek, Paul |
author_facet | Ritchie, Graham RS Flicek, Paul |
author_sort | Ritchie, Graham RS |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4254438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-42544382014-12-04 Computational approaches to interpreting genomic sequence variation Ritchie, Graham RS Flicek, Paul Genome Med Review 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. BioMed Central 2014-10-22 /pmc/articles/PMC4254438/ /pubmed/25473426 http://dx.doi.org/10.1186/s13073-014-0087-1 Text en © Ritchie and Flicek; licensee BioMed Central Ltd. 2014 The licensee has exclusive rights to distribute this article, in any medium, for 12 months following its publication. After this time, the article is available under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Ritchie, Graham RS Flicek, Paul Computational approaches to interpreting genomic sequence variation |
title | Computational approaches to interpreting genomic sequence variation |
title_full | Computational approaches to interpreting genomic sequence variation |
title_fullStr | Computational approaches to interpreting genomic sequence variation |
title_full_unstemmed | Computational approaches to interpreting genomic sequence variation |
title_short | Computational approaches to interpreting genomic sequence variation |
title_sort | computational approaches to interpreting genomic sequence variation |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254438/ https://www.ncbi.nlm.nih.gov/pubmed/25473426 http://dx.doi.org/10.1186/s13073-014-0087-1 |
work_keys_str_mv | AT ritchiegrahamrs computationalapproachestointerpretinggenomicsequencevariation AT flicekpaul computationalapproachestointerpretinggenomicsequencevariation |