A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?
data integration
data science
functional genomics
machine learning
systems biology
Journal
Trends in biochemical sciences
ISSN: 0968-0004
Titre abrégé: Trends Biochem Sci
Pays: England
ID NLM: 7610674
Informations de publication
Date de publication:
01 2019
01 2019
Historique:
received:
02
07
2018
revised:
19
10
2018
accepted:
25
10
2018
pubmed:
14
12
2018
medline:
4
4
2019
entrez:
8
12
2018
Statut:
ppublish
Résumé
High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now changing with the appearance of resources to help bench-side biologists become skilled at computational data analysis and handling large omics data sets. Here, we show an entry route into the field of omics data analytics. We provide information about easily accessible data sources and suggest some first steps for aspiring computational data analysts. Moreover, we highlight how machine learning is transforming the field and how it can help make sense of biological data. Finally, we suggest good starting points for self-learning and hope to convince readers that computational data analysis and programming are not intimidating.
Identifiants
pubmed: 30522862
pii: S0968-0004(18)30226-3
doi: 10.1016/j.tibs.2018.10.010
pmc: PMC6318833
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
21-32Subventions
Organisme : Wellcome Trust
ID : 103139/Z/13/Z
Pays : United Kingdom
Informations de copyright
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
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