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Expression Atlas
Expression Atlas (4) is a database of analysed gene and protein expression data that contains two components:
a. Baseline Atlas – providing gene and protein expression data for normal, untreated tissues or commonly used cell lines;
b. Differential Atlas – allowing queries on genes that are up- or down- regulated in different experimental conditions.
Unlike ArrayExpress which focuses on experiments, Expression Atlas focuses on genes/proteins and biological conditions, allowing you to ask biological questions such as:
- What genes are expressed in normal human liver?
- What genes are expressed across a panel of ENCODE cell lines?
- What genes are up- or down-regulated in drought and salt tolerance (DST) mutant Japanese rice plants vs wild type controls?
- What genes are up- or down-regulated in bovine skin samples from tick-resistant vs tick-susceptible animals following tick infestation?
Explore the Expression Atlas home page by clicking on the 
Figure 4 The Expression Atlas home page. You can browse all differential and baseline experiments or search for data by gene, gene property, species or biological condition.
How Expression Atlas is produced
The Atlas is composed of a sub-set of data sets from ArrayExpress, namely those on expression profiling, which are manually curated and then analysed in-house by a standard statistical pipeline. For proteomics data, we take analysed (processed) data from trusted collaborators and do not currently perform our own in-house analysis, but the meta-data (e.g. sample annotation) still go through the same rigorous level of manual curation step to ensure they are accurate and unambiguous.
The manual curation step ensures only well-annotated data sets from well-designed experiments are included in the Atlas. For example, for an experiment to be considered for the differential atlas, it must have at least three biological replicates for each condition for proper downstream statistical analysis, and the intent of the experiment must be clear. Various quality-control metrics are also implemented during statistical analysis to discard sub-standard data, e.g. microarray data with lots of background noise.
 Learn more in our Expression Atlas: Quick tour |