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Mining SAGE data allows large-scale, sensitive screening of antisense transcript expression.

Ronan Quéré1, Laurent Manchon, Mireille Lejeune

  • 1Institut de Génétique Humaine, UPR CNRS 1142, 141 rue de la Cardonille, 34396 Montpellier, France.

Nucleic Acids Research
|November 25, 2004
PubMed
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Serial Analysis of Gene Expression (SAGE) can identify antisense transcripts by analyzing complementary DNA sequences. This method reveals novel regulatory RNA and enhances understanding of gene expression across numerous biological samples.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • Eukaryotes express numerous complementary transcripts with regulatory functions.
  • High-throughput methods are crucial for studying these transcripts' expression in biological samples.

Purpose of the Study:

  • To develop and validate a method for detecting antisense transcripts using existing Serial Analysis of Gene Expression (SAGE) datasets.
  • To explore the utility of SAGE for investigating the expression of antisense transcripts.

Main Methods:

  • Serial Analysis of Gene Expression (SAGE) was employed, focusing on the enumeration of short cDNA sequences (tags).
  • Antisense transcripts were initially identified by tags mapping to the reverse complement of known mRNAs.
  • A computational approach using virtual tags was developed to mine SAGE data for antisense transcript evidence, analyzing complementarity between sequences.

Related Experiment Videos

Main Results:

  • SAGE datasets were shown to contain latent information on antisense transcripts.
  • Analysis revealed transcripts expressed from both strands of adjacent genes, as well as mutually exclusive or co-expressed transcripts from cis-oriented genes.
  • Overlapping transcripts from trans-encoded genes and tags shared by multiple transcripts, including those mapping to retroelements like Alu sequences, were identified.

Conclusions:

  • The developed method demonstrates that SAGE datasets are a valuable resource for sensitive and rapid investigation of antisense transcript expression.
  • This approach enables the detection of single tags in numerous libraries, facilitating large-scale screening.
  • The findings highlight the potential of SAGE for uncovering complex regulatory RNA interactions.