Tutorial
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- Changes in this version
- Class comparison. Worked examples and exercises
- Class prediction
- Class prediction. Worked examples and exercises
- Clustering
- Clustering. Worked examples and exercises
- Cross hybridization
- data matrix expression
- Data types
- Define your comparison
- Detailed example of analysis of expression data in Babelomics: from raw data to expression differential and functional profiling
- Differential Expression for arrays
- Differential Expression for RNA Seq
- Dye bias
- Edit
- Edit your data
- example data
- Expression
- Expression array pipeline
- FAQ
- Functional
- Functional Gene Set Network Enrichment
- Functional GO Enrichment
- GAL
- Gene Set Enrichment
- Gene Set Network Enrichment (Network Miner)
- Gene vs annotation
- Genepix
- Genomics
- Genomics doc
- How to cite babelomics
- Id
- Logging in
- Main areas. Cancer
- Main areas. Expression
- Main areas. Functional
- Main areas. Genomics
- Main areas. Processing
- Main areas: Cancer
- Main areas: Expression
- Main areas: Functional
- Main areas: Genomics
- Main areas: Processing
- Network Enrichment (SNOW)
- Other biological data
- Overview and pipelines
- p values adjusted for multiple testing
- PED
- PED_MAP zipped
- Pipelines
- plink.assoc
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- plink.fisher
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- plink.tdt
- Preprocessing for data matrix
- Preprocessing for microarrays
- Preprocessing for RNA Seq
- Processing
- Ranked
- Requirements
- RNA Seq Normalization
- RNA Seq pipeline
- SDK (Software Development Kit)
- Single Enrichment
- Single Enrichment. Options
- SNPs array pipeline
- Software and databases used
- Technical Info
- The Babelomics Team
- tut_SNP_association
- Tutorial
- Tutorial Affymetrix Expression Microarray Normalization
- Tutorial Agilent One Color Microarray Normalization
- Tutorial Agilent Two Colors Microarray Normalization
- Tutorial Burden test
- Tutorial Class prediction
- Tutorial Clustering
- Tutorial Data matrix preprocessing
- Tutorial Differential Expression for arrays
- Tutorial Differential Expression for RNA Seq
- Tutorial Expression
- Tutorial Expression. Class comparison
- Tutorial Expression. Correlation
- Tutorial Expression. Survival
- Tutorial Functional
- Tutorial Genepix One Color Microarray Normalization
- Tutorial Genepix Two Colors Microarray Normalization
- Tutorial Genomics
- Tutorial OncodriveClust
- Tutorial OncodriveFM
- Tutorial Processing
- Tutorial SNP Association Analysis
- Tutorial SNP stratification
- Upload your data
- VCF 4.0
- VCF file pipeline
- Visualization tools
- Worked examples
- Workflow
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General
Tutorial
Analysis tools
Worked examples
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Expression
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Functional
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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
Getting started
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.
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Logging in: Learn how to create an account, how to log in and the difference between a registered and an anonymous user.
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Babelomics web structure: Discover the Babelomics web structure.
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Workflow: Find out the general workflow to follow for running the Babelomics tools with your own data.
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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.
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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.
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Data types: See all data types that Babelomics deals with; either as input data or as a result output.
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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.
Analysis Tools
The analysis tools included in Babelomics are divided in five main working areas:
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Processing. Includes the Edit tool, the normalization of expression array and RNA-Seq data and the pre-processing of other kind of biological data.
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Expression. Includes the Clustering, Class comparison and Differential Expression tools.
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Genomics. Includes the Association analysis and the Burden test tools.
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Cancer. Includes the OncodriveFM and OncodriveClust tools.
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Functional. Includes the functional tools of Babelomics: Single enrichment, Gene set enrichment, Network enrichment and Gene set network enrichment.
Find the Babelomics suite at http://babelomics.org