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s ·nr: a visual analytics framework for contextual analyses of private and public RNA-seq data
Paul Klemm1, Peter Frommolt2, Jan-Wilhelm Kornfeld3
1Max Planck Institute for Metabolism Research, Gleuler Str. 50, Cologne, 50931, Germany. paul.klemm@sf.mpg.de.
BMC Genomics
|January 26, 2019
Summary
Researchers can now analyze Next-Generation Sequencing (NGS) data more effectively with s·nr, a visual analytics tool. This tool enables exploration of personal and public RNA-Seq data to generate novel hypotheses.
Area of Science:
- Molecular Biology
- Bioinformatics
- Computational Biology
Background:
- Next-Generation Sequencing (NGS) is crucial in molecular biology.
- Despite cost reductions and automated pipelines, interpreting vast NGS data remains challenging.
- Existing methods lack sophistication for comprehensive data analysis.
Purpose of the Study:
- To introduce s·nr, a Visual Analytics tool for NGS data interpretation.
- To provide researchers with intuitive interfaces for displaying and querying NGS data.
- To enable exploration of personal data alongside public repositories.
Main Methods:
- Development of s·nr, a Visual Analytics tool.
- Utilizing containerized implementation for easy deployment.
- Testing on 1543 RNA-Seq mouse differential expression profiles from ArrayExpress.
Main Results:
- s·nr offers powerful visual interfaces for NGS data exploration.
- The tool facilitates querying and extraction of datasets with similar gene expression signatures.
- Demonstrated capability to analyze personal RNA-Seq data in the context of public data.
Conclusions:
- s·nr empowers researchers to analyze and relate their RNA-Seq data.
- Facilitates interactive crosstalk with public repositories for data contextualization.
- Enables deduction of novel, unbiased hypotheses regarding molecular processes.
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