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Updated: Mar 14, 2026

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Bioinformatics Pipeline for Transcriptome Sequencing Analysis.

Sarah Djebali¹, Valentin Wucher², Sylvain Foissac³

¹INRA GenPhySE, ch. de Borderouge, Castanet-Tolosan, 31326, France. sarahqd@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|September 25, 2016

Summary

This summary is machine-generated.

This study presents a bioinformatics protocol for analyzing RNA sequencing (RNA-seq) data. The workflow uses STAR, Cufflinks, and RSEM to process transcriptome sequencing data for gene expression quantification.

Keywords:

Bioinformatics workflow Protocols RNA-seq Transcriptome sequencing

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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Published on: April 8, 2017

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

High Throughput Sequencing (HTS) for RNA profiling (RNA-seq) reveals transcriptome diversity.
RNA-seq is a standard for monitoring expressed transcripts but generates massive data.
Processing RNA-seq data requires specialized bioinformatics tools and protocols.

Purpose of the Study:

To describe a standard bioinformatics protocol for RNA-seq data analysis.
To present a workflow for transcriptome reconstruction and gene expression quantification.
To demonstrate the protocol using human transcriptome sequencing data.

Main Methods:

Utilized the STAR aligner for mapping sequencing reads to a reference genome.
Employed Cufflinks for transcriptome reconstruction.
Used RSEM for quantifying gene and transcript expression levels.

Main Results:

A standardized bioinformatics protocol for RNA-seq data analysis was established.
The workflow successfully processed human transcriptome sequencing data from the ENCODE3 project.
Gene and transcript expression levels were quantified.

Conclusions:

The described protocol provides a robust method for analyzing RNA-seq data.
This workflow enables efficient processing of large-scale transcriptome sequencing datasets.
The protocol is applicable to various research contexts, including cell line studies.