A Primer on Data Analytics in Functional Genomics: How to Move from Data to Insight?

Piotr Grabowski, Juri Rappsilber

Research output: Contribution to journalComment/debatepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)21-32
Number of pages12
JournalTrends in biochemical sciences
Issue number1
Early online date3 Dec 2018
Publication statusPublished - 1 Jan 2019

Keywords / Materials (for Non-textual outputs)

  • data integration
  • data science
  • functional genomics
  • machine learning
  • systems biology


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