Expression Atlas update—an integrated database of gene and protein expression in humans, animals and plants

1Expression Atlas is a further development of its predecessor, Gene Expression Atlas launched by the European Bioinformatics Institute (EMBL-EBI) in 2008, and continues its original remit as a value-added database for querying gene expression across tissues, cell types and cell lines under various biological conditions. These include developmental stages, physiological states, phenotypes and diseases, and covers nearly 30 organisms including metazoans and plants. Expression Atlas is developed with a view to accommodating data from multi-omics experiments; the first proteomics data set has been included in 2015.

2High-quality microarray and RNA-sequencing (RNA-seq) data in Expression Atlas continue to come from ArrayExpress, which also includes data imported from NCBI's Gene Expression Omnibus (GEO). Expression is reported for both coding and non-coding transcripts. The sample attributes and experimental variables are carefully curated, systematized and mapped to the Experimental Factor Ontology (EFO) for efficient search via ontology-driven query expansion, and to facilitate data integration with other resources.

3Expression Atlas consists of two components—(i) a large baseline expression component (http://www.ebi.ac.uk/gxa/baseline/experiments), reporting transcript abundance estimates for each gene in healthy or untreated tissues, cell types or cellular components from carefully selected large RNA-seq experiments and (ii) information about the changes in transcript abundance between two different conditions, such as normal and disease.

4Since the last update, we have included in the baseline Atlas a number of important projects such as Human Protein Atlas and The Genotype-Tissue Expression (GTEx) project. New funding sources and user feedback have accelerated the expansion of Atlas into disparate data domains, for example plants and cancer. For the first time, Atlas contains 389 experiments studying plants in 11 species (http://www.ebi.ac.uk/gxa/plant/experiments), e.g. rice, wheat, maize and Arabidopsis, including 7 baseline studies reporting expression in tissues, strains and cultivars. 97 differential and 3 baseline experiments in Atlas study cancer.

5Atlas’ ability to display expression across all tissues and all baseline studies next to each other in a single, intuitive interface makes it easy for the user to spot corroborating patterns of expression across multiple ‘omics studies. All differential expression data are now also available for further analysis as R objects.

6Annotations reported by expression studies may become defunct due to addition of new transcribed loci or dropping invalid entries in updated genomic references. To address this, Atlas release cycle is synchronised with that of Ensembl, Ensembl Genomes (including Ensembl Plants) and the Gramene databases, guaranteeing the latest gene annotations, microarray probe-set mappings and genomic references. For each new genome assembly, all RNA-seq data in Atlas in the corresponding organism are re-processed to match the most current version of the reference genome. Recent examples of using Expression Atlas data for novel research include, for example, references.

7At the time of writing, Expression Atlas contains highly curated data from 1572 studies (69239 assays), incorporating RNA-seq based baseline expression (http://www.ebi.ac.uk/gxa/baseline/experiments, 36376 assays) in tissues from Human Protein Atlas (http://www.ebi.ac.uk/gxa/experiments/E-MTAB-2836), GTEx (http://www.ebi.ac.uk/gxa/experiments/E-MTAB-2919), FANTOM5 (, http://www.ebi.ac.uk/gxa/experiments/E-MTAB-3358), in cancer cell lines from ENCODE (, http://www.ebi.ac.uk/gxa/experiments/E-GEOD-26284), Cancer Cell Line Encyclopaedia (CCLE,, http://www.ebi.ac.uk/gxa/experiments/E-MTAB-2770) and Genentech (, http://www.ebi.ac.uk/gxa/experiments/E-MTAB-2706) projects, as well as differential expression for manually curated comparisons (4287 as of August 2015). Table 1 shows the top 15 organisms in Atlas with the highest number of studies. Examples of plant data in Atlas include several studies of rice salt stress, for example time-course experiments studying Oryza sativa japonica cv. Nipponbare (salt-sensitive) variety: http://www.ebi.ac.uk/gxa/experiments/E-MTAB-1625 (RNA-seq) and http://www.ebi.ac.uk/gxa/experiments/E-MTAB-1624 (microarray), allowing for comparison of expression obtained from the same physical samples using different technologies. This line was chosen because the reference rice genome was also sequenced from it. Users can also view the baseline expression profile of genes from a gene family or a given pathway from Plant Reactome Wikipathways. For example, Figure 4 shows rice auxin efflux (PIN) and auxin influx (AUX) gene family members participating in Auxin (IAA) transport pathway in a plant cell.

8Expression Atlas is intended as a multi-omics, and in particular as a functional genomics and proteomics, resource. Since both the transcript and peptide molecules undergo their own independent modifications as well as degradation in a spatial temporal manner, providing both kinds of data provides an opportunity for researchers to asses spatial temporal and condition based correlation of transcript amount versus the amount of its translated product estimated from proteomics experiments. While the quantitation and statistical analysis of transcript expression methods are relatively mature and well established, the equivalent methods for protein detection, quantification and statistical analysis are still active areas of research. Consequently, in the first instance, we have included our first protein expression data (http://www.ebi.ac.uk/gxa/experiments/E-PROT-1) as additional information to the transcriptomics data in the baseline component of Expression Atlas only, shown side by side for the corresponding tissues. This proteomics study consists of re-analysed mass spectrometry raw data from the draft map of the human proteome, downloaded from the PRIDE repository (PXD000561), and comprising 85 experimental samples from 30 human adult and fetal tissues.

9Since the last update, we have adopted Tophat2 and HTSeq for genome reference alignment and gene expression quantification respectively, for all RNA-seq experiments in Atlas. We have currently suspended reporting baseline expression for splice variants for several reasons—first, uncertainty about the reliability of the methods currently available, second, careful research has shown that for most genes in most conditions there is one dominant isoform expressed, and finally because of the high computational requirements.

10Expression Atlas continues to analyse and report statistically significant differential gene expression in manually curated differential pairwise comparisons between two sets of biological replicates—the ‘reference’ (e.g. ‘healthy’ or ‘wild type’) set and a ‘test’ set (e.g. ‘diseased’ or ‘mutant’). The differential analysis is now performed using DESeq2 with independent filtering. Since the last update we have also included parameterization of additional factors and blocking effects where possible, thus eliminating technical sources of variation and boosting statistical power in studies with heterogeneous sample sets. Consequently, we were able to load into Atlas 50 new studies, including clinical ones containing detailed patient histories.

11Users now have more tools at their disposal to assess the accuracy of expression data reported by Atlas: (i) the number of biological replicates is now reported for a given baseline condition, or on either side of a differential comparison; (ii) for a given baseline condition, quantile normalisation is used to make distributions of expressions in each biological replicate the same—prior to averaging gene expression levels across biological replicates; (iii) for a given gene-condition, users can view a box plot of variability of baseline expression across biological replicates, providing them with an impression of how representative the reported median expression level is.

12Expression Atlas remains committed to the stringent quality control of raw experimental data and design, reported in the previous update. Since then we have automated quality control of RNA-seq data, that involves exclusion of corrupted FASTQ files and those with an insufficient number of reads after filtering for poor quality and contamination. As in the case of microarray outlier array removal, removing poor quality data files may lead to the exclusion of affected differential comparisons, baseline conditions or even whole experiments from Atlas.

13The proteomics data analysis methods are described in the Human Proteome Label Free Analysis section of the supporting material.

14Expression Atlas search interface allows for querying one or more genes or proteins from a selected species. The user can also add search filters for sample attributes and experimental factors, taking full advantage of ontology-driven query expansion. For example, searching for disease lymphoma will return expression data from samples of not only lymphoma itself, but also from its subtypes and closely related diseases, e.g. Hodgkin's lymphoma or acute myelogenic leukemia. Using the same interface, both baseline and differential components of Expression Atlas are queried by default. The Atlas interface displays search results from all tissues and all baseline studies making it possible to find patterns of expression across a wide variety of studies (Figure 1). We are working to extend this functionality to other types of experimental conditions for which Atlas has wealth of baseline expression data, e.g. cell lines, as well as to comparative views of expression, highlighting common tissue expression patterns for orthologues—across all available baseline data sets. The interface also showcases more detailed anatomical images, in which tissues with reported expression are highlighted. This now includes a separate brain diagram for human and mouse, as well as ‘whole plant’ and ‘flower parts’ diagrams for plant experiments.

15Various novel analyses and visualisations have been implemented in Atlas. For example, the overlap between the set of differentially expressed genes in each Atlas comparison, and Reactome and Plant Reactome pathways, GO terms and InterPro domains is now assessed using Fisher's test with multiple testing correction. The resulting pathways, terms or domains that are ‘enriched’ in a given comparison are shown in network-style visualisations, including the effect size (Figure 2). For each baseline study, a visualisation of hierarchical clustering between the 100 most variable genes and experimental conditions is also shown. Finally, for a given gene-condition, the user can view a box plot of variability of baseline expression across biological replicates (Figure 3).

16Expression data from Atlas are now viewable as tracks on Ensembl, Ensembl Genomes and Gramene genome browsers. Baseline expression data from Atlas are also automatically included in Ensembl, Ensembl Genomes, Gramene Ensembl Plants, Reactome and Plant Reactome, via javascript-based widgets. The widgets are easily accessible (https://github.com/gxa/atlas/blob/master/web/src/main/javascript/heatmap/README.md) and can be integrated in any third-party site, provided the bioentity identifiers match those of the Atlas.

17We plan to include in Atlas the latest data from ENCODE, GTEx version 5, Blueprint (http://dcc.blueprint-epigenome.eu/), NIH Epigenomic Roadmap and HipSci (http://www.hipsci.org/).

18A number of new proteomics studies will be loaded into Atlas in the near future.

19Users will be able to perform on-the-fly overlap analysis between their provided set of genes and differentially expressed gene sets in each comparison in Atlas, resulting in a (sorted by effect size) list of comparisons in which the user provided gene set is ‘enriched’.

20For a given gene within a single study, we will enable the user to find other genes of similar expression profile across experimental conditions or differential comparisons.

21We plan to make available baseline expression of orthologues in tissues.

22We plan to provide exon quantifications for all RNA-seq experiments. We will also re-visit the topic of splice-variant expression quantification by benchmarking several splice-variant expression quantification methods (namely, Kallisto and RSEM), with the plan to bring splice variant quantification back into Atlas once the computational and accuracy issues are resolved satisfactorily.

23We plan to extend our analysis pipelines and visualisation methods to adequately annotate, quality control and visualise gene expression data from single-cell RNA-seq studies.

24We plan to enable handling of additional factors or blocking effects for baseline expression in the near future.

25We plan to make baseline expression data available for download as R objects. We will also create an R package in Bioconductor for accessing all Atlas data.

26We are always listening to the feedback from our users, and the future plans will be adjusted according to the user requirements.

27Supplementary Data are available at NAR Online.