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Updated: May 28, 2026
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
Published on: May 28, 2021
GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences
Jason S Cumbie1, Jeffrey A Kimbrel, Yanming Di
1Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America.
Plos One
|October 15, 2011
Summary
GENE-counter is a versatile pipeline for RNA-Sequencing (RNA-Seq) data analysis, enabling differential gene expression studies in diverse organisms. It offers robust statistical methods and transparent data management for reliable results.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- RNA-Sequencing (RNA-Seq) is crucial for transcriptomic analysis.
- Analyzing differential gene expression requires robust computational tools.
- Existing tools may have limitations with non-model organisms or small sample sizes.
Purpose of the Study:
- To introduce GENE-counter, a comprehensive Perl-based computational pipeline for RNA-Seq data analysis.
- To provide a flexible tool applicable to various organisms, including prokaryotes and non-model organisms.
- To offer robust statistical methods for differential gene expression analysis and gene ontology enrichment.
Main Methods:
- GENE-counter utilizes Perl for its computational pipeline.
- Supports multiple alignment tools (CASHX, Bowtie, BWA) and any Sequence Alignment/Map (SAM)-compliant program.
- Incorporates three statistical packages based on the negative binomial distribution, including a novel default method.
- Includes three methods for gene ontology (GO) term enrichment analysis.
- Stores results in a MySQL relational database for systematic data management and quality assessment.
Main Results:
- GENE-counter successfully processed a small-scale RNA-Seq dataset from Arabidopsis thaliana.
- Demonstrated support for analysis of microarrays alongside RNA-Seq data.
- Showcased substantial overlap in results across different statistical packages, indicating robustness.
- Validated suitability for handling small sample sizes and high gene count variability.
Conclusions:
- GENE-counter is a well-suited computational pipeline for differential gene expression analysis of RNA-Seq data.
- Its flexibility makes it applicable to a wide range of organisms and experimental conditions.
- The pipeline ensures transparent results and facilitates further data analysis through systematic storage.
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03:21RNA-seq
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