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Mapping and quantifying mammalian transcriptomes by RNA-Seq.

Ali Mortazavi1, Brian A Williams, Kenneth McCue

  • 1Division of Biology, MC 156-29, California Institute of Technology, Pasadena, California 91125, USA.

Nature Methods
|June 3, 2008
PubMed
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This study maps mouse transcriptomes using RNA-sequencing, providing digital gene expression data. Researchers identified novel gene models and detected numerous alternative splicing events, enhancing our understanding of gene regulation.

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Accurate transcriptome quantification is crucial for understanding gene expression.
  • Existing methods have limitations in detecting novel transcripts and splice variants.

Purpose of the Study:

  • To map and quantify mouse transcriptomes using deep sequencing (RNA-Seq).
  • To establish reference measurements for gene expression in key mouse tissues.
  • To identify novel gene models and alternative splicing events.

Main Methods:

  • Deep sequencing of poly(A)-selected RNA from adult mouse brain, liver, and skeletal muscle.
  • RNA-Seq data analysis to map reads and quantify transcript prevalence.
  • Utilized RNA standards for accurate quantification and to determine the linear detection range.

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Main Results:

  • Generated 41-52 million mapped reads per tissue, providing digital transcriptome measurements.
  • Over 90% of reads mapped to known exons, but remaining data indicated new gene models and transcript variants.
  • Detected 1.45 x 10^5 distinct RNA splice events, with 3,500 genes showing alternative splicing.

Conclusions:

  • RNA-Seq offers a powerful digital approach for comprehensive transcriptome analysis.
  • The study identified novel gene structures and a high prevalence of alternative splicing in mice.
  • This work provides a valuable reference dataset for mouse gene expression studies.