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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Updated: May 15, 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

TranSeqAnnotator: large-scale analysis of transcriptomic data.

Ranjeeta Menon¹, Gagan Garg, Robin B Gasser

¹Department of Chemistry and Biomolecular Sciences and ARC Centre of Excellence, Macquarie University, Sydney, NSW 2109, Australia.

BMC Bioinformatics

|January 4, 2013

Summary

This summary is machine-generated.

TranSeqAnnotator is a bioinformatics pipeline for analyzing expressed sequence tag (EST) data. It automates the process of cleaning, assembling, and annotating transcriptomic data to identify potential therapeutic targets.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Last Updated: May 15, 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

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Bioinformatics
Transcriptomics
Computational Biology

Background:

Expressed sequence tag (EST) data analysis provides a rapid, cost-effective method for studying organism transcriptomes.
Advancements in large-scale sequencing necessitate automated pipelines for managing and annotating vast amounts of sequence data.
Comprehensive analysis involves integrating transcriptome, genome, and proteome data.

Purpose of the Study:

To develop an automated workflow for large-scale transcriptomic data analysis.
To integrate appropriate bioinformatics tools for efficient data management and annotation.
To identify potential therapeutic targets from expressed sequence tag datasets.

Main Methods:

TranSeqAnnotator is a workflow that cleans, clusters, and assembles ESTs and short reads.
It performs conceptual translation to predict protein products and assigns putative functions via similarity searches.
Annotation includes nucleotide, protein, and excretory/secretory (ES) protein levels.

Main Results:

The pipeline automatically processes raw and quality ESTs, protein, and short read sequences.
It generates consensus sequences and identifies potential protein products with assigned functions.
Excretory/secretory (ES) proteins are identified from ESTs and short reads.

Conclusions:

TranSeqAnnotator provides exhaustive and reliable analysis and detailed annotation of large EST datasets.
Outputs include gene ontologies, protein functional identifications, domain mapping, and metabolic pathway assignments.
The tool identified novel and known genes with therapeutic potential, serving as targets for parasite intervention.