Babelomics is an integrated web-based suite of tools for the analysis of genomic data. It includes methods for high throughput data preprocessing, normalization, clustering, differential gene expression and class prediction. Babelomics also implements a complete suite of web tools for advanced functional profiling of transcriptomic, proteomic and genomic experiments. You can find here tools for GO Enrichment, Gene Set GO Enrichment, Network Enrichment and Gene Set Network Enrichment.
Here you will find tutorials, worked examples, exercises, publications, news and much more information about Babelomics.
Overview & working pipelines
The general pipeline of Babelomics can be easily visualized in graphic below:
However, you can incorporate your data at any step of the main pipeline as the tools can work independently of each other. Thus, users can perform shorter pipelines specific for their necessities. You can find a complete description of each step and the corresponding available tools along this documentation wiki.
Babelomics working pipelines for expression array, RNA-Seq, SNPs array, VCF files and other kind of biological data are available. Remember that you can also directly upload the already processed data necessary to run a particular Babelomics tool.
- Expression array pipeline
- RNA-Seq pipeline
- SNPs array pipeline
- VCF file pipeline
- Other biological data
Learn how to use the Babelomics web tool in 5 minutes!
These short tutorials will guide you through your firsts steps in Babelomics. They provide specific information about the general use of the Babelomics web tool.
Logging in: Learn how to create an account, how to log in and the difference between a registered and an anonymous user.
Babelomics web structure: Discover the Babelomics web structure.
Workflow: Find out the general workflow to follow for running the Babelomics tools with your own data.
Upload your data: Uploading data is the first step of every analysis pipeline. Learn how to upload your own data to Babelomics and manage the workspace.
Edit your data: Discover how to edit your own data, which is the way to introduce information about the experimental design and the data factors into Babelomics.
Data types: See all data types that Babelomics deals with; either as input data or as a result output.
Visualization tools: Visualization can help in the interpretation of your results. Find out what visualization tools are integrated in Babelomics and how they can improve your analysis.
The analysis tools included in Babelomics are divided in five main working areas:
Processing. Includes the Edit tool, the normalization of expression array and RNA-Seq data and the pre-processing of other kind of biological data.
Expression. Includes the Clustering, Class comparison and Differential Expression tools.
Genomics. Includes the Association analysis and the Burden test tools.
Cancer. Includes the OncodriveFM and OncodriveClust tools.
Functional. Includes the functional tools of Babelomics: Single enrichment, Gene set enrichment, Network enrichment and Gene set network enrichment.