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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

TAGADA: a scalable pipeline to improve genome annotations with RNA-seq data.

Cyril Kurylo¹, Cervin Guyomar¹, Sylvain Foissac¹

¹GenPhySE, Université de Toulouse, INRAE, INPT, ENVT, Toulouse, France.

NAR Genomics and Bioinformatics

|October 18, 2023

Summary

This summary is machine-generated.

TAGADA is a new RNA-seq pipeline that improves genome annotation by assembling, deconvoluting, and analyzing transcripts and genes. This bioinformatics tool enhances gene models, calculates expression values, and identifies long non-coding RNAs (lncRNAs).

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Last Updated: Jul 13, 2025

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

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Area of Science:

Bioinformatics
Genomics
Transcriptomics

Background:

Genome annotation is vital for cataloging species-specific genes and transcripts.
Integrating new transcriptome data into existing annotations is essential but challenging for current bioinformatics pipelines.
Existing pipelines struggle with effectively and consistently updating annotations using new RNA-sequencing (RNA-seq) data.

Purpose of the Study:

To introduce TAGADA, a novel RNA-sequencing (RNA-seq) pipeline designed for Transcripts And Genes Assembly, Deconvolution, and Analysis.
To enhance existing gene models and generate improved genome annotations using RNA-seq data.
To provide a comprehensive tool for expression value computation, long non-coding RNA (lncRNA) identification, and quality control.

Main Methods:

TAGADA processes genomic sequences, reference annotations, and RNA-seq reads.
The pipeline utilizes Nextflow DSL2 for user-friendly functionality and containerization for reproducibility.
It integrates short reads (Illumina NovaSeq) with long reads (PacBio Iso-Seq) for versatile analysis.

Main Results:

TAGADA substantially increases the number of annotated transcripts, by approximately [FORMULA] in chicken and pig genomes.
The pipeline successfully computes expression values for both reference and novel annotations.
TAGADA identifies long non-coding transcripts (lncRNAs) and provides a detailed quality control report.

Conclusions:

TAGADA offers an effective and reproducible solution for updating and improving genome annotations using RNA-seq data.
The pipeline's ability to integrate diverse read types (short and long) enhances its versatility.
TAGADA significantly advances the field of comparative genomics and transcriptomics by improving gene model comprehensiveness.