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Related Experiment Videos

Linking cDNA-AFLP-based gene expression patterns and ESTs.

Ling Qin1, Pjotr Prins, Johannes Helder

  • 1Laboratory of Microbiology-Fungal Genomics, Wageningen University and Research Center, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)
|November 3, 2005
PubMed
Summary
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A new computer program, GenEST, efficiently links DNA sequence databases with gene expression profiles. This tool aids researchers in identifying genes based on their expression patterns using expressed sequence tag (EST) and cDNA-amplified fragment length polymorphism (AFLP) data.

Area of Science:

  • Bioinformatics
  • Molecular Biology
  • Genomics

Background:

  • Vast amounts of DNA sequence data from expressed sequence tag (EST) and genome sequencing projects necessitate efficient methods for integration with expression profiles.
  • Linking sequence databases with temporal and spatial gene expression data is crucial for understanding gene function and regulation.

Purpose of the Study:

  • To develop a computational tool, GenEST, for linking cDNA sequence data with transcript profiles obtained from cDNA-amplified fragment length polymorphism (AFLP).
  • To enable efficient screening of cDNA sequence databases for genes exhibiting specific expression patterns.

Main Methods:

  • Development of the GenEST computer program for in silico prediction of transcript derived fragment (TDF) sizes from cDNA sequences.
  • Utilizing cDNA-AFLP, a reproducible differential display technique based on restriction enzyme digestion and selective amplification.

Related Experiment Videos

  • Establishing a traceback mechanism to link virtual TDFs predicted by GenEST to actual TDFs on cDNA-AFLP gels.

    • Main Results:

    • GenEST successfully predicts virtual TDF sizes from cDNA sequences, facilitating their identification among thousands of TDFs on cDNA-AFLP gels.
    • The program enables efficient screening of cDNA sequence databases to identify genes with relevant expression profiles.
    • Conversely, GenEST allows identification of corresponding DNA sequences for TDFs with significant expression patterns.

    Conclusions:

    • GenEST provides a powerful and efficient solution for integrating large-scale DNA sequence data with gene expression information.
    • This approach significantly enhances the ability to discover and analyze genes based on their expression profiles, advancing functional genomics research.