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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.
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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: Jun 23, 2025

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

RNA-Seq Data Analysis.

James Li¹, Rency S Varghese¹, Habtom W Ressom²

¹Genomics & Epigenomics Shared Resource, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA.

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

|June 22, 2024

Summary

This summary is machine-generated.

This chapter details a comprehensive RNA-sequencing (RNA-Seq) data analysis pipeline, covering quality control, preprocessing, alignment, differential expression, and functional analysis. It also introduces advanced machine learning applications for deeper biological insights from genomics data.

Keywords:

Differential expression Next-generation sequencing Sequence alignment Transcriptomics

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Last Updated: Jun 23, 2025

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

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Published on: February 2, 2024

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

RNA-sequencing (RNA-Seq) is crucial for modern genomics research.
Analyzing complex RNA-Seq data requires a structured and comprehensive approach.
Rapid advancements necessitate updated analysis methodologies.

Purpose of the Study:

To present a detailed pipeline for RNA-Seq data analysis.
To cover essential steps from data quality control to advanced machine learning applications.
To provide a foundational understanding for researchers working with RNA-Seq data.

Main Methods:

Quality control and preprocessing of raw sequence data.
Alignment of processed sequences to a reference genome.
Differential gene expression analysis and functional enrichment (e.g., Gene Ontology, pathway analysis).
Application of machine learning techniques, including dimension reduction and supervised/unsupervised learning.

Main Results:

A systematic workflow for RNA-Seq data analysis is elucidated.
Key steps ensure data integrity and accurate mapping.
Identification of differentially expressed genes and their biological context is achieved.
Machine learning reveals complex patterns in RNA-Seq data.

Conclusions:

The described pipeline offers a robust framework for RNA-Seq data interpretation.
Integrating machine learning enhances the discovery potential of RNA-Seq analyses.
A thorough understanding of these methods is vital for advancing genomics research.