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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

A simple guide to de novo transcriptome assembly and annotation.

Venket Raghavan¹, Louis Kraft¹, Fantin Mesny²

¹Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, 37077 Göttingen, Germany.

Briefings in Bioinformatics

|January 25, 2022

Summary

De novo transcriptome assembly and annotation from RNA-sequencing data is crucial for gene discovery without a reference genome. This overview simplifies complex procedures and tool selection for researchers.

Keywords:

RNA-seq annotation assembly de novo tools transcriptome

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Published on: May 9, 2017

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Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Short-read RNA sequencing (RNA-seq) provides a proxy catalog of protein-coding genes.
De novo transcriptome assembly is essential when genome sequencing is infeasible or costly.
Accurate transcriptome assembly and annotation are vital for identifying gene functions and evolutionary features.

Purpose of the Study:

To provide a comprehensive overview of de novo transcriptome assembly and annotation.
To elucidate the procedures, including pre- and post-processing steps.
To present a compendium of available tools for transcriptome analysis.

Main Methods:

Utilizing RNA-sequencing reads for de novo transcriptome reconstruction.
Implementing annotation pipelines to identify sequence features.
Reviewing and categorizing various bioinformatics tools for assembly and annotation.

Main Results:

De novo transcriptome assembly and annotation present significant challenges due to tool complexity and lack of standardization.
Numerous pre- and post-processing steps are involved in achieving accurate results.
A wide array of tools exists, complicating workflow selection for researchers.

Conclusions:

This work offers a structured approach to navigating the complexities of de novo transcriptome assembly and annotation.
The presented overview and tool compendium aim to assist researchers in selecting appropriate methods and software.
Standardization and clear guidelines are needed to facilitate the efficient use of RNA-seq data for gene discovery.