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Related Experiment Videos

Discrimination of non-protein-coding transcripts from protein-coding mRNA.

Martin C Frith1, Timothy L Bailey, Takeya Kasukawa

  • 1Genome Exploration Research Group (Genome Network Project Core Group), RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, Kanagawa, Japan.

RNA Biology
|November 23, 2006
PubMed
Summary

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This summary is machine-generated.

Scientists evaluated ten computational methods to distinguish protein-coding from non-coding RNA transcripts. Most methods agreed, confirming many novel transcripts are non-coding, but also finding errors in protein databases.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Recent studies reveal numerous RNA transcripts lacking known gene origins in mammals and other organisms.
  • These transcripts are hypothesized to be non-coding RNAs, but distinguishing them from protein-coding sequences is challenging.
  • Existing methods for this discrimination vary, with unclear reliability.

Purpose of the Study:

  • To evaluate and compare the performance of ten bioinformatic methods for discriminating protein-coding from non-coding RNA transcripts.
  • To assess the congruency of these methods in identifying coding and non-coding sequences.
  • To identify factors contributing to unreliable predictions and suggest improvements.

Main Methods:

  • Analysis of ten bioinformatic tools assessing protein-coding potential.

Related Experiment Videos

  • Comparison based on four principles: open reading frame size, sequence similarity to known proteins, statistical models, and substitution rates.
  • Evaluation of transcript discrimination capabilities and method concordance.

    • Main Results:

    • Broad concordance observed among the ten methods, supporting reliable discrimination of coding and non-coding transcripts.
    • A significant portion of recently discovered extra-genic transcripts are confirmed as non-coding.
    • Unexpectedly, approximately 10% of entries in the Swiss-Prot database appear to be erroneous translations of non-coding transcripts.

    Conclusions:

    • Bioinformatic methods can reliably distinguish coding from non-coding RNA transcripts, validating many novel findings.
    • The study highlights potential errors in curated protein databases, necessitating re-evaluation.
    • Understanding transcript coding potential is crucial for accurate genomic annotation and functional studies.