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Related Experiment Video

Updated: Feb 26, 2026

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis.

Raquel L Costa^1,2, Luiz Gadelha¹, Marcelo Ribeiro-Alves³

¹DEXL Lab, National Laboratory for Scientific Computing (LNCC), Petrópolis, Rio de Janeiro, Brazil.

|July 12, 2017

Summary

This summary is machine-generated.

GeNNet integrates transcriptome analysis workflows with graph databases, simplifying gene selection and data exploration for researchers. This platform enhances biological data analysis by unifying tools and managing complex experimental results.

Keywords:

Data-to-knowledge GeNNet Graph database Microarray Provenance Scientific workflow Software container Transcriptome

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Published on: April 8, 2017

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Transcriptome data analysis involves multiple complex steps from raw data acquisition to gene subset selection.
Integrating diverse biological databases and analytical tools into scientific workflows presents significant challenges, leading to fragmented results.
Managing experimental data and metadata, along with orchestrating complex in-silico experiments, requires substantial effort.

Purpose of the Study:

To present GeNNet, an integrated platform unifying scientific workflows and graph databases for transcriptome analysis.
To provide a comprehensive solution for pre-processing, analyzing, and visualizing transcriptome data, overcoming fragmentation issues.
To enable easier exploration of gene interaction networks and hypothesis testing through an interactive graph database.

Main Methods:

GeNNet-Wf: A scientific workflow for data pre-processing, normalization, differential expression, clusterization, and gene set enrichment analysis.
GeNNet-Web: A user-friendly web interface for parameter setting, execution, and result visualization.
GeNNet-DB: A graph database for integrating and querying experimental results, gene relationships, and metadata.

Main Results:

Case studies using GEO data demonstrated GeNNet's ability to identify differentially expressed genes and analyze their biological functions.
The integrated graph database (GeNNet-DB) effectively supports reasoning about gene interaction networks.
GeNNet successfully processed transcriptome data from human, rhesus, mouse, and rat samples from Affymetrix platforms.

Conclusions:

GeNNet is the first platform to combine transcriptome analysis workflows with graph databases, offering a unified approach.
The platform simplifies the installation and use of complex bioinformatics tools for non-expert users.
GeNNet facilitates hypothesis testing and exploration of new biological insights through its integrated analytical and database environment.