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Related Concept Videos

RNA-seq03:21

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...
Ribosome Profiling02:24

Ribosome Profiling

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.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

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Related Experiment Video

Updated: May 26, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

RNASEQR--a streamlined and accurate RNA-seq sequence analysis program.

Leslie Y Chen1, Kuo-Chen Wei, Abner C-Y Huang

  • 1Institute for Systems Biology, Seattle, WA 98109, USA. lchen@systemsbiology.org

Nucleic Acids Research
|December 27, 2011
PubMed
Summary

RNASEQR accurately maps RNA-sequencing (RNA-seq) data for improved gene expression analysis. This tool enhances the discovery of transcript isoforms and genetic variations from next-generation sequencing experiments.

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AQRNA-seq for Quantifying Small RNAs
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Last Updated: May 26, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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AQRNA-seq for Quantifying Small RNAs
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AQRNA-seq for Quantifying Small RNAs

Published on: February 2, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Next-generation sequencing (NGS) based RNA-sequencing (RNA-seq) is crucial for studying gene expression and variations.
  • RNA-seq analysis faces challenges in accurate genome alignment and biological information extraction.
  • High-quality alignment is fundamental for reliable RNA-seq data interpretation.

Purpose of the Study:

  • To develop RNASEQR, a novel tool for accurate and effective mapping of RNA-seq sequences.
  • To systematically compare RNASEQR's performance against existing widely used RNA-seq alignment tools.
  • To assess RNASEQR's capability in improving gene expression estimation, transcript structure determination, and variant detection.

Main Methods:

  • Development of the RNASEQR software for RNA-seq sequence mapping.
  • Systematic comparison using simulated data from the Consensus CDS project.
  • Validation with two experimental RNA-seq datasets from a human glioblastoma patient.

Main Results:

  • RNASEQR demonstrated superior accuracy in gene expression estimation compared to four other leading tools.
  • The tool provided more complete gene structures and identified novel transcript isoforms effectively.
  • RNASEQR achieved higher accuracy in detecting single nucleotide variants (SNVs).
  • RNASEQR efficiently processes raw RNA-seq data for downstream analyses.

Conclusions:

  • RNASEQR offers a significant advancement in RNA-seq data analysis, providing more accurate biological insights.
  • The tool's compatibility with various downstream analyses makes it valuable for researchers.
  • RNASEQR effectively addresses key informatics challenges in transcriptomic profiling.