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Big knowledge from big data in functional genomics

With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate func...

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Detalles Bibliográficos
Autor principal: Ponting, Chris P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288990/
https://www.ncbi.nlm.nih.gov/pubmed/33525805
http://dx.doi.org/10.1042/ETLS20170129
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author Ponting, Chris P.
author_facet Ponting, Chris P.
author_sort Ponting, Chris P.
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description With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base.
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spelling pubmed-72889902020-06-18 Big knowledge from big data in functional genomics Ponting, Chris P. Emerg Top Life Sci Commentaries With so much genomics data being produced, it might be wise to pause and consider what purpose this data can or should serve. Some improve annotations, others predict molecular interactions, but few add directly to existing knowledge. This is because sequence annotations do not always implicate function, and molecular interactions are often irrelevant to a cell's or organism's survival or propagation. Merely correlative relationships found in big data fail to provide answers to the Why questions of human biology. Instead, those answers are expected from methods that causally link DNA changes to downstream effects without being confounded by reverse causation. These approaches require the controlled measurement of the consequences of DNA variants, for example, either those introduced in single cells using CRISPR/Cas9 genome editing or that are already present across the human population. Inferred causal relationships between genetic variation and cellular phenotypes or disease show promise to rapidly grow and underpin our knowledge base. Portland Press Ltd. 2017-11-14 2017-11-14 /pmc/articles/PMC7288990/ /pubmed/33525805 http://dx.doi.org/10.1042/ETLS20170129 Text en © 2017 The Author(s) https://creativecommons.org/licenses/by/4.0/ This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and the Royal Society of Biology and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Commentaries
Ponting, Chris P.
Big knowledge from big data in functional genomics
title Big knowledge from big data in functional genomics
title_full Big knowledge from big data in functional genomics
title_fullStr Big knowledge from big data in functional genomics
title_full_unstemmed Big knowledge from big data in functional genomics
title_short Big knowledge from big data in functional genomics
title_sort big knowledge from big data in functional genomics
topic Commentaries
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7288990/
https://www.ncbi.nlm.nih.gov/pubmed/33525805
http://dx.doi.org/10.1042/ETLS20170129
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