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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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Related Experiment Video

Updated: Mar 27, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

Published on: December 2, 2009

Highly sensitive and ultrafast read mapping for RNA-seq analysis.

I Medina¹, J Tárraga², H Martínez³

¹HPC Service, UIS, University of Cambridge, Cambridge, UK.

DNA Research : an International Journal for Rapid Publication of Reports on Genes and Genomes

|January 8, 2016

Summary

This summary is machine-generated.

New RNA-sequencing (RNA-seq) analysis methods improve transcriptome mapping sensitivity and speed. This bioinformatics approach enhances data analysis for massive sequencing, overcoming current limitations in RNA-seq mapping tools.

Keywords:

Burrows-Wheeler Transform RNA-seq high-performance computing mapping

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Related Experiment Videos

Last Updated: Mar 27, 2026

A Rapid High-throughput Method for Mapping Ribonucleoproteins RNPs on Human pre-mRNA

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Published on: December 2, 2009

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Published on: August 4, 2016

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

Published on: November 18, 2014

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Sequencing technologies generate vast amounts of data, making data analysis a critical bottleneck.
Current RNA-sequencing (RNA-seq) mapping tools lack optimal sensitivity and performance for efficient transcriptome analysis.
Massive sequencing initiatives require advanced bioinformatics solutions for accurate and rapid data interpretation.

Purpose of the Study:

To develop an improved mapping solution for RNA-sequencing data.
To enhance the sensitivity and performance of transcriptome analysis.
To address the limitations of existing RNA-seq mapping methods.

Main Methods:

A novel approach combining re-engineering, optimization, and parallelization was developed.
The method was designed to handle large-scale transcriptome data from massive sequencing.
Performance was evaluated against current RNA-seq mapping techniques.

Main Results:

The innovative approach significantly increased mapping sensitivity across various read lengths.
Substantial reductions in runtime were achieved compared to existing RNA-seq mapping methods.
The solution demonstrated improved efficiency for transcriptome analysis.

Conclusions:

The developed method offers a significant advancement in RNA-seq data analysis.
This approach enhances the efficiency and accuracy of transcriptome mapping.
It provides a valuable tool for researchers utilizing massive sequencing data.