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Related Experiment Videos

CDS annotation in full-length cDNA sequence.

Masaaki Furuno1, Takeya Kasukawa, Rintaro Saito

  • 1Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.

Genome Research
|June 24, 2003
PubMed
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Accurate coding sequence (CDS) prediction is crucial for gene function annotation. This study developed a strategy using multiple prediction tools for high-throughput cDNA sequences, achieving high-quality CDS annotations for FANTOM2 clones.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Coding sequence (CDS) identification is vital for gene functional annotation.
  • Predicting mammalian CDS from genomic sequences is challenging due to abundant intergenic regions.
  • High-throughput cDNA (HTC) sequences present unique complexities for CDS prediction.

Purpose of the Study:

  • To develop and implement a robust CDS annotation strategy for FANTOM2 high-throughput cDNA sequences.
  • To improve the accuracy and reliability of CDS predictions in large cDNA datasets.

Main Methods:

  • Utilized a multi-program CDS annotation strategy for FANTOM2 cDNAs.
  • Integrated various prediction tools: rsCDS (similarity-based), ProCrest, Longest-ORF, Truncated-ORF (ab initio), and DECODER, NCBI CDS predictor (hybrid).

Related Experiment Videos

  • Employed graphical displays for CDS prediction results and sequence similarity, alongside manual curation and quality control.

    • Main Results:

    • Successfully annotated CDS regions for 14,345 FANTOM2 clones.
    • Achieved high-quality CDS predictions through a comprehensive annotation strategy.
    • Demonstrated the effectiveness of combining multiple prediction tools and manual inspection.

    Conclusions:

    • The developed CDS annotation strategy yields high-quality predictions for large-scale cDNA projects.
    • Integrating diverse prediction methods and expert curation enhances the accuracy of gene functional annotation.
    • This approach is valuable for annotating complex transcriptomic datasets like FANTOM2.