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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Explorative visual analytics on interval-based genomic data and their metadata.

Vahid Jalili¹, Matteo Matteucci², Marco Masseroli²

¹Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy. vahid.jalili@polimi.it.

BMC Bioinformatics

|December 6, 2017

Summary

This summary is machine-generated.

This study introduces GenoMetric Space Explorer (GeMSE), a tool for interactive analysis and visualization of next-generation sequencing (NGS) data. GeMSE aids biologists and bioinformaticians in making sense of complex genomic datasets through seamless data exploration and analysis.

Keywords:

Comparative evaluation Genomic data analysis Interactive and visual analytics Next Generation Sequencing exploration visualization

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Public repositories of next-generation sequencing (NGS) processed data are widespread.
Effective tools for data exploration, analysis, and visualization are crucial for interpreting this data.
Interactive analytics facilitate seamless data sense-making by integrating analysis and visualization.

Purpose of the Study:

To present abstractions for early analysis of NGS processed data.
To introduce the GenoMetric Space Explorer (GeMSE) tool for interactive analytics.
To support the GenoMetric Query Language and enable analysis of heterogeneous genomic datasets.

Main Methods:

Implementation of abstractions in the GenoMetric Space Explorer (GeMSE) tool.
Support for standard genomic file formats (BED, BroadPeak, NarrowPeak, GTF) and tab-delimited files.
Interactive analytics with on-the-fly cycling through exploration, analysis, and visualization steps.
Explorative interaction support for tracing activities and recovering results.

Main Results:

GeMSE enables interactive analytics for making sense of heterogeneous genomic datasets.
The tool supports seamless backward and forward navigation through analysis steps.
Comparative visualizations of heatmaps are facilitated.
GeMSE can process data from standard genomic formats and custom tab-delimited files.

Conclusions:

GeMSE demonstrates practical usefulness through significant biological use cases.
The tool is available for use and its source code is open-source.
GeMSE enhances the ability of biologists and bioinformaticians to analyze and interpret NGS data.