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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
Published on: May 22, 2018
Methods for visual mining of genomic and proteomic data atlases
John Boyle1, Richard Kreisberg, Ryan Bressler
1Institute for Systems Biology, 401 Terry Ave N, Seattle, WA 98092, USA. john.boyle@systemsbiology.org
BMC Bioinformatics
|April 25, 2012
Summary
New visual mining tools help researchers analyze massive biological datasets, enabling faster insights from complex proteomics and genomics data. These intuitive tools support systems biology research and the exploration of large-scale atlases like PeptideAtlas and The Cancer Genome Atlas.
Area of Science:
- Bioinformatics and Systems Biology
- Computational Biology
- Genomics and Proteomics
Background:
- Increasing data volume and complexity in scientific research necessitates advanced analytical software.
- Traditional tools often lack the intuitive interface required for high-level scientific reasoning and complex data interaction.
- Information visualization offers a solution by enabling direct manipulation and interaction with diverse datasets.
Purpose of the Study:
- To develop visual mining tools for analyzing massive datasets in systems biology research.
- To provide intuitive and easy-to-use tools for exploring complex proteomics and genomics data.
- To address the challenge of applying information visualization to rapidly evolving bioinformatics fields.
Main Methods:
- Development of visual mining tools for large-scale data collections.
- Creation of interactive tools for exploring proteomics (PeptideAtlas) and genomics (The Cancer Genome Atlas) datasets.
- Utilizing common visualization workflows and metaphors for rapid tool deployment and user understanding.
Main Results:
- Tools enable visual mining of thousands of mass spectrometry experiments for biomarker identification.
- Interactive analysis of hundreds of genomes to explore variations across cancer types.
- Facilitation of targeted exploration from diseases to single genes within large biological atlases.
Conclusions:
- New tools and techniques are essential for mining massive biological data repositories.
- Visual exploration of large-scale atlas data allows for new insights from single samples to population scales.
- Rapid development and deployment of linked, task-specific visual tools accelerate data analysis and inference in systems biology.
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