Expression Atlas: gene and protein expression across multiple studies and organisms
Irene Papatheodorou1, Nuno A Fonseca1, Maria Keays1
1European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Hinxton, UK.
Nucleic Acids Research
|November 23, 2017
Summary
Expression Atlas is a database for gene and protein expression across species and conditions. It offers data visualization, download, and analysis tools for researchers studying biological contexts.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Gene and protein expression data are crucial for understanding biological processes.
- Integrating diverse expression datasets aids comprehensive analysis.
- Databases providing curated and standardized expression information are essential for research.
Purpose of the Study:
- To present the Expression Atlas database, detailing its scope, features, and data accessibility.
- To highlight the integration of large-scale RNA sequencing studies within the database.
- To showcase novel analytical and visualization tools for exploring gene expression patterns.
Main Methods:
- Curating and re-analyzing public and controlled-access gene and protein expression datasets.
- Utilizing standardized, open-source bioinformatics pipelines for data processing.
- Developing web-based and R package interfaces for data querying, download, and visualization.
Main Results:
- Expression Atlas contains data from 3,126 studies across 33 species, including 731 plant studies, as of August 2017.
- The database integrates data from major initiatives like Blueprint, PCAWG, ENCODE, GTEx, and HipSci.
- Users can query gene expression across various contexts (species, tissues, developmental stages, diseases) and download processed data matrices.
- New features include on-the-fly gene set overlap analysis and gene co-expression visualization.
Conclusions:
- Expression Atlas serves as a valuable resource for exploring and analyzing gene and protein expression data.
- The database facilitates comparative analysis across diverse biological contexts and species.
- Enhanced features and accessibility through an R package improve data utilization for the research community.
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