Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10868
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Main Title: A primer on data analytics in functional genomics: How to move from data to insight?
Author(s): Grabowski, Piotr
Rappsilber, Juri
Type: Article
Is Part Of: 10.14279/depositonce-9070
Language Code: en
Abstract: High-throughput technologies are now widely used in the life sciences field and are producing ever-increasing amounts and diversity of data. While many laboratories and even undergraduate students generate high-throughput data, analyzing these results requires a skill set that is traditionally reserved for bioinformaticians. Learning to program using languages such as R and Python and making sense of the vast amounts of available omics data have become easier, thanks to the multitude of available resources. This can empower bench-side researchers to perform more complex computational analyses. Tools such as KNIME or Galaxy (together with a growing number of tutorials and courses) have been crucial in providing simple user interfaces to conduct complex analyses under the hood, making the ‘big data’ revolution accessible to biologists. 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.
URI: https://depositonce.tu-berlin.de/handle/11303/11988
http://dx.doi.org/10.14279/depositonce-10868
Issue Date: 3-Dec-2018
Date Available: 18-Nov-2020
DDC Class: 570 Biowissenschaften; Biologie
Subject(s): data integration
data science
functional genomics
machine learning
systems biology
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Trends in Biochemical Sciences
Publisher: Cell Press
Publisher Place: Amsterdam [u.a.]
Volume: 44
Issue: 1
Publisher DOI: 10.1016/j.tibs.2018.10.010
Page Start: 21
Page End: 32
EISSN: 1362-4326
ISSN: 0968-0004
Appears in Collections:FG Bioanalytik » Publications

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