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RNA-seq

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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.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Updated: Nov 16, 2025

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

Published on: February 2, 2024

RNA-Seq in Nonmodel Organisms.

Vered Chalifa-Caspi¹

¹Bioinformatics Core Facility, Ben-Gurion University of the Negev, Beer-Sheva, Israel. veredcc@bgu.ac.il.

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

|February 19, 2021

Summary

This summary is machine-generated.

This chapter details RNA-Seq data analysis for nonmodel organisms, addressing challenges like missing genomes. It guides users through de novo assembly, annotation, and functional enrichment for comprehensive transcriptome profiling.

Keywords:

Annotation De novo assembly Differential expression Expression profiling Functional enrichment Nonmodel organisms Pathway analysis RNA-Seq Transcriptome

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Area of Science:

Bioinformatics
Genomics
Transcriptomics

Background:

RNA-Sequencing (RNA-Seq) is crucial for transcriptome profiling in both model and nonmodel organisms.
Analyzing RNA-Seq data from nonmodel organisms presents unique challenges due to the lack of high-quality genome references and limited downstream analysis tools.

Purpose of the Study:

To provide a comprehensive overview of RNA-Seq data analysis steps specifically for nonmodel organisms.
To elaborate on unique challenges and strategic decisions in analyzing transcriptomes without a reference genome.

Main Methods:

Strategic planning for sequencing technology and reference genome usage.
Methods for searching, improving gene prediction, and annotating draft genomes.
Read cleaning, de novo transcriptome assembly, quality assessment, and redundancy reduction.
Functional annotation, transcript abundance quantification, and functional enrichment testing.
Utilizing various software tools (Linux, R packages) and the NeatSeq-Flow platform.

Main Results:

A structured approach to overcome challenges in nonmodel organism RNA-Seq analysis.
Guidance on de novo transcriptome assembly and quality control.
Strategies for functional annotation and enrichment analysis applicable to diverse species.

Conclusions:

This chapter offers a practical guide and workflow for robust RNA-Seq analysis in nonmodel organisms.
It empowers researchers to conduct detailed transcriptome profiling even with limited genomic resources.
The presented methods and tools facilitate deeper biological insights from nonmodel organism transcriptomes.