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Reuse of public genome-wide gene expression data

Key Points

  • Over the past decade, high-throughput gene expression experiments have generated data from millions of assays. Data sets linked to publications are stored in functional genomics data archives: ArrayExpress at the European Bioinformatics Institute, Gene Expression Omnibus at the US National Center for Biotechnology Information and at the DNA Databank of Japan Omics Archive.

  • Secondary added-value and topical databases process data from the primary archives, adding analysis and annotation to make these data accessible to every biologist by allowing queries such as 'in which tissue is a particular gene expressed?' or 'which genes are differentially expressed between a particular disease and normal samples?'

  • Public gene expression data are commonly reused to study biological questions, both by reanalysis of primary data and by queries to secondary resources. Approximately half of the studies that use public gene expression data rely solely on existing data without adding newly generated data, and half of them use the public data in combination with new data.

  • The reproducibility of published microarray-based studies is limited, mostly owing to insufficient experiment annotation and sometimes to unavailability of the raw or processed data. A stricter enforcement of Minimum Information About a Microarray Experiment (MIAME) requirements and also development of easy-to-use experiment annotation tools are needed to achieve a better reproducibility.

  • Although most of the public gene expression data still are based on microarray experiments, the contribution of high-throughput-sequencing-based expression studies, known as RNA sequencing (RNA-seq), are growing rapidly.

  • Reuse of RNA-seq data can potentially be even more valuable than reuse of microarray data, partly owing to the costs of experiments and data storage but even more importantly because of a more quantitative nature of sequencing-based expression data. Community standards such as Minimum Information about Sequencing Experiments (MINSEQE) should be adopted to make RNA-seq data maximally reusable.

  • The bioinformatics resources that store and manage public data are sensitive to short-term funding changes, complicating the maintenance of important databases. The development of long-term infrastructure in bioinformatics, such as the ELIXIR project in Europe, is needed to ensure the long term availability of public data.

Abstract

Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments — microarrays and next-generation sequencing — have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.

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Acknowledgements

We would like to thank H. Parkinson and U. Sarkans for useful comments and help in analysing ArrayExpress statistics. The work was partly funded by the European Community's FP7 HEALTH grants ENGAGE (grant agreement 201413), SYBARIS (grant agreement 242220) and EurocanPlatform (grant agreement 260791).

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Correspondence to Alvis Brazma.

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ArrayExpress

FGED — MINSEQE

Gene Expression Atlas

Gene Expression Omnibus

MIAME

Glossary

Microarray

A solid surface slide on which a collection of microscopic DNA spots representing specific DNA sequences of genomic regions are attached and to which sample DNA fragments can hybridize. Microarrays are used to measure the expression levels of large numbers of genes simultaneously, to genotype multiple regions of a genome or for other high-throughput assays.

Minimum Information About a Microarray Experiment

(MIAME). A guideline for information that is necessary for the unambiguous interpretation of the results of the experiment, potentially allowing the reproduction of the experiment. MIAME postulates that raw and processed data, sample annotation, array feature annotation, relationship between the samples used in the experiment, arrays and data files, the overall description of the experiment and experimental variables must be given in a usable format to make the results of a microarray experiment interpretable.

Gene Expression Omnibus

(GEO). A public functional genomics data repository supporting MIAME-compliant data submissions at the US National Center for Biotechnology Information accepting array- and sequence-based data.

ArrayExpress

A MIAME-compliant archive of functional genomics data at the European Bioinformatics Institute. It is one of the international public data archives recommended by scientific journals for depositions of microarray or high-throughput sequencing data related to publications.

High-throughput sequencing

DNA sequencing technologies that parallelize the sequencing operations, thus achieving several magnitudes higher throughput than the traditional sequencing methods based on processes invented by Fred Sanger.

RNA sequencing

(RNA-seq). The use of high-throughput sequencing technologies applied to cDNA molecules obtained by reverse transcription from RNA, or sequencing RNA directly, in order to get information about the RNA content of a sample.

Meta-analysis

Refers to methods focused on contrasting and combining results from different studies to identify common patterns and improving the signal in data by combining multiple studies.

Normalization

In relation to microarray and other high-throughput data, normalization usually refers to data transformations that remove systematic noise and that make data combined from several assays mutually comparable.

Minimum Information about a Sequencing Experiment

(MINSEQE). A formulation of the information that is necessary to interpret the results of a sequencing experiment unambiguously and potentially to reproduce the experiment. MINSEQE is an adoption of Minimum Information About a Microarray Experiment guidelines to functional genomics experiments based on RNA sequencing and other high-throughput-sequencing-based functional genomics experiments.

ELIXIR

A life sciences infrastructure project that unites Europe's leading life sciences organizations in managing and safeguarding the massive amounts of data being generated every day by publicly funded research.

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Rung, J., Brazma, A. Reuse of public genome-wide gene expression data. Nat Rev Genet 14, 89–99 (2013). https://doi.org/10.1038/nrg3394

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