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Related Concept Videos

RNA-seq03:21

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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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...
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Updated: May 1, 2026

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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PRADA: pipeline for RNA sequencing data analysis.

Wandaliz Torres-García1, Siyuan Zheng1, Andrey Sivachenko1

  • 1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, The Eli and Edythe L. Broad Institute of Harvard University and MIT, Cambridge, MA 02142 and Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10015, USA.

Bioinformatics (Oxford, England)
|April 4, 2014
PubMed
Summary
This summary is machine-generated.

PRADA is a new software platform for analyzing RNA-sequencing data. This pipeline automates the processing of large datasets, providing comprehensive gene expression and fusion transcript information.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • High-throughput sequencing generates massive datasets.
  • Existing computational tools struggle with systematic, automated analysis.
  • Need for advanced pipelines for RNA-sequencing data.

Purpose of the Study:

  • To develop a flexible, modular, and scalable software platform for RNA-sequencing data analysis.
  • To automate the processing of raw paired-end RNA-seq data.
  • To provide multifaceted information including gene expression and fusion transcript detection.

Main Methods:

  • Developed PRADA (Pipeline for RNA-Sequencing Data Analysis).
  • Implemented a dual-mapping strategy for increased sensitivity.
  • Designed for systematic and automated analysis of large-scale datasets.

Main Results:

  • PRADA provides gene expression levels and quality metrics.
  • Detects unsupervised and supervised fusion transcripts, including intragenic variants.
  • Classifies fusion frames and provides homology scores.

Conclusions:

  • PRADA is a robust tool for processing and analyzing large-scale RNA-seq data.
  • The dual-mapping strategy enhances analytical precision.
  • Successfully applied in major cancer genome projects.