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From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data.

Jilong Li¹, Jie Hou², Lin Sun³

¹Computer Science Department, University of Missouri, Columbia, Missouri, United States of America; MU Botanical Center, University of Missouri, Columbia, Missouri, United States of America.

|April 23, 2015

Summary

This summary is machine-generated.

RNAMiner is a new bioinformatics pipeline that analyzes RNA sequencing data to identify gene expression changes and build gene regulatory networks. This tool helps extract valuable biological insights from large datasets across multiple species.

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Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Next-generation sequencing (NGS) generates vast amounts of RNA reads.
Analyzing large RNA sequencing (RNA-Seq) datasets requires advanced bioinformatics protocols.
Current methods need improvement for comprehensive biological knowledge extraction from RNA-Seq data.

Purpose of the Study:

To develop and present RNAMiner, a multi-level bioinformatics protocol and pipeline.
To facilitate the mining of large RNA-Seq datasets for biological insights.
To provide a robust tool for gene expression analysis and network construction.

Main Methods:

RNAMiner protocol involves five key steps: read mapping, gene expression calculation, differential gene expression identification, gene function prediction, and gene regulatory network construction.
The pipeline was applied to RNA-Seq datasets from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster.
Utilized next-generation sequencing (NGS) data for analysis.

Main Results:

Successfully identified differentially expressed genes across diverse species.
Clustered genes into functionally related groups, revealing biological pathways.
Constructed novel gene regulatory networks, offering new insights into gene interactions.
Demonstrated the utility of RNAMiner on multiple model organisms.

Conclusions:

RNAMiner is an effective tool for comprehensive analysis of RNA-Seq data.
The pipeline facilitates the discovery of gene expression patterns and regulatory relationships.
RNAMiner provides a valuable resource for biological research, available as a web service.