Dintor: functional annotation of genomic and proteomic data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics and Proteomics

Background:

• Large-scale genomics and proteomics datasets are increasingly available.
• High-throughput sequencing costs are decreasing, leading to more researchers entering the field.
• Analyzing these datasets is challenging due to data heterogeneity and the need for integration.

Purpose of the Study:

• To develop a user-friendly computational framework for analyzing genomics and proteomics data.
• To provide a set of tools that address common data analysis tasks.
• To ensure reproducibility of results by managing data versions.

Main Methods:

• Implementation of Dintor, a data integration framework.
• Development of over 30 specialized tools for various analysis tasks.
• Creation of a mechanism for linking tools with specific dataset versions for reproducibility.

Main Results:

• Dintor provides over 30 tools for genomics and proteomics data exploration.
• The framework supports tasks like gene identifier conversion, orthology mapping, functional annotation, and variant analysis.
• Dintor ensures reproducible results through version management of datasets.
• The software is available as open-source and via a public Galaxy web service.

Conclusions:

• Dintor is a valuable computational framework for genomic and proteomic data analysis.
• It offers a comprehensive suite of tools for frequently encountered tasks.
• A key advantage is its ability to ensure reproducible research through consistent dataset version handling.