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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. 
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Related Experiment Video

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Identification of novel transcripts in annotated genomes using RNA-Seq.

Adam Roberts1, Harold Pimentel, Cole Trapnell

  • 1Department of Computer Science, UC Berkeley, Berkeley, CA, USA.

Bioinformatics (Oxford, England)
|June 24, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for assembling novel RNA sequences (RNA-Seq) using existing gene annotations. The developed algorithm aids in discovering new transcripts by comparing RNA-Seq data against reference annotations.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Transcriptomics

Background:

  • RNA sequencing (RNA-Seq) enables the discovery of novel transcripts.
  • Leveraging existing annotations is crucial for analyzing gene expression in model organisms.
  • Identifying novel transcripts alongside known ones is a key challenge in transcriptomics.

Purpose of the Study:

  • To define and address the problem of reference annotation-based transcript assembly for RNA-Seq data.
  • To develop and present an algorithm for assembling novel transcripts within the context of existing annotations.
  • To demonstrate the utility of the algorithm in investigating novel transcripts identified by RNA-Seq.

Main Methods:

  • Development of a novel algorithm for reference annotation-based transcript assembly.
  • Application of the algorithm to RNA-Seq data from model organisms.
  • Comparison of novel transcripts with existing reference annotations.

Main Results:

  • Successfully defined the 'reference annotation based transcript assembly' problem.
  • Presented a functional algorithm for this novel problem.
  • Demonstrated the algorithm's capability to rapidly investigate novel transcripts.

Conclusions:

  • The proposed method and algorithm effectively address the challenge of assembling novel transcripts using reference annotations.
  • This approach facilitates the discovery and analysis of new transcripts in RNA-Seq studies.
  • The Cufflinks software suite implements these methods for RNA-Seq analysis.