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The role of functional data in interpreting the effects of genetic variation
Progress in DNA-sequencing technologies has provided a catalogue of millions of DNA variants in the human population, but characterization of the functional effects of these variants has lagged far behind. For example, sequencing of tumor samples is driving an urgent need to classify whether or not...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The American Society for Cell Biology
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710221/ https://www.ncbi.nlm.nih.gov/pubmed/26543197 http://dx.doi.org/10.1091/mbc.E15-03-0153 |
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author | Young, David L. Fields, Stanley |
author_facet | Young, David L. Fields, Stanley |
author_sort | Young, David L. |
collection | PubMed |
description | Progress in DNA-sequencing technologies has provided a catalogue of millions of DNA variants in the human population, but characterization of the functional effects of these variants has lagged far behind. For example, sequencing of tumor samples is driving an urgent need to classify whether or not mutations seen in cancers affect disease progression or treatment effectiveness or instead are benign. Furthermore, mutations can interact with genetic background and with environmental effects. A new approach, termed deep mutational scanning, has enabled the quantitative assessment of the effects of thousands of mutations in a protein. However, this type of experiment is carried out in model organisms, tissue culture, or in vitro; typically addresses only a single biochemical function of a protein; and is generally performed under a single condition. The current challenge lies in using these functional data to generate useful models for the phenotypic consequences of genetic variation in humans. |
format | Online Article Text |
id | pubmed-4710221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | The American Society for Cell Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-47102212016-01-20 The role of functional data in interpreting the effects of genetic variation Young, David L. Fields, Stanley Mol Biol Cell Big Data Progress in DNA-sequencing technologies has provided a catalogue of millions of DNA variants in the human population, but characterization of the functional effects of these variants has lagged far behind. For example, sequencing of tumor samples is driving an urgent need to classify whether or not mutations seen in cancers affect disease progression or treatment effectiveness or instead are benign. Furthermore, mutations can interact with genetic background and with environmental effects. A new approach, termed deep mutational scanning, has enabled the quantitative assessment of the effects of thousands of mutations in a protein. However, this type of experiment is carried out in model organisms, tissue culture, or in vitro; typically addresses only a single biochemical function of a protein; and is generally performed under a single condition. The current challenge lies in using these functional data to generate useful models for the phenotypic consequences of genetic variation in humans. The American Society for Cell Biology 2015-11-05 /pmc/articles/PMC4710221/ /pubmed/26543197 http://dx.doi.org/10.1091/mbc.E15-03-0153 Text en © 2015 Young and Fields. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0). “ASCB®,” “The American Society for Cell Biology®,” and “Molecular Biology of the Cell®” are registered trademarks of The American Society for Cell Biology. |
spellingShingle | Big Data Young, David L. Fields, Stanley The role of functional data in interpreting the effects of genetic variation |
title | The role of functional data in interpreting the effects of genetic variation |
title_full | The role of functional data in interpreting the effects of genetic variation |
title_fullStr | The role of functional data in interpreting the effects of genetic variation |
title_full_unstemmed | The role of functional data in interpreting the effects of genetic variation |
title_short | The role of functional data in interpreting the effects of genetic variation |
title_sort | role of functional data in interpreting the effects of genetic variation |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4710221/ https://www.ncbi.nlm.nih.gov/pubmed/26543197 http://dx.doi.org/10.1091/mbc.E15-03-0153 |
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