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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.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

Next-generation Sequencing

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Updated: Jun 22, 2026

AQRNA-seq for Quantifying Small RNAs

AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Transcriptome sequencing of the Microarray Quality Control (MAQC) RNA reference samples using next generation

Shrinivasrao P Mane¹, Clive Evans, Kristal L Cooper

¹Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA. rvjensen@vt.edu

|June 16, 2009

Summary

This summary is machine-generated.

Deep transcriptome sequencing using Roche

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

Genomics and Transcriptomics
Next-Generation Sequencing Technologies

Background:

Next-generation sequencing (NGS) platforms are emerging as a competitive alternative to DNA microarrays for global gene expression analysis.
The Microarray Quality Control (MAQC) project provided reference RNA samples for technology evaluation.

Purpose of the Study:

To evaluate the performance of deep transcriptome sequencing using Roche's 454 Genome Sequencer FLX.
To compare gene expression analysis by deep sequencing with established methods like DNA microarrays and Quantitative RT-PCR (QRTPCR).

Main Methods:

Deep sequencing of cDNA from MAQC reference RNA samples using Roche's 454 Genome Sequencer FLX.
Development of a data analysis pipeline to translate cDNA read counts into gene expression levels.
Alignment of sequence reads to the human genome and RefSeq database using BLAST and AceView programs.

Main Results:

Generated over 3.6 million sequence reads, with 90% mapping to the human genome and 64% to the RefSeq database.
Gene expression levels demonstrated excellent reproducibility, sensitivity, and specificity, comparable to DNA microarrays and QRTPCR.
Identified 137,899 unique exon junctions, including 22,193 novel ones, revealing transcriptome complexity.

Conclusions:

Deep transcriptome sequencing offers high quantitative accuracy, reproducibility, sensitivity, and specificity for gene expression analysis.
Increased sequencing depth systematically improves performance metrics.
This approach provides novel insights into human transcriptome complexity and splice variant discovery.