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Related Experiment Videos

fRNAdb: a platform for mining/annotating functional RNA candidates from non-coding RNA sequences.

Taishin Kin1, Kouichirou Yamada, Goro Terai

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST) Aomi 2-42, Koto-ku, Tokyo 135-0064, Japan. kin-taushin@aist.go.jp

Nucleic Acids Research
|November 14, 2006
PubMed
Summary
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Computational analysis helps identify functional non-coding RNA candidates from uncharacterized transcripts. The fRNAdb database offers a resource for mining these potential functional RNAs, reducing costly experimental validation.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Numerous transcripts lack protein-coding function and remain functionally uncharacterized.
  • Distinguishing functional non-coding RNAs from junk transcripts is challenging.
  • Experimental validation of these transcripts is resource-intensive.

Purpose of the Study:

  • To computationally identify candidate functional non-coding RNA transcripts.
  • To develop a centralized database resource for non-coding RNA analysis.
  • To provide tools for filtering and prioritizing potential functional RNAs.

Main Methods:

  • Compilation of non-coding transcripts from H-inv, NONCODE, and RNAdb into fRNAdb.
  • Performance of computational analyses including RNA secondary structure motif discovery, EST support evaluation, cis-regulatory element search, and protein homology search.

Related Experiment Videos

  • Development of a user-friendly interface for transcript filtering.

    • Main Results:

    • The fRNAdb database integrates diverse non-coding RNA data.
    • Computational analyses provide insights into potential functions of uncharacterized transcripts.
    • fRNAdb facilitates the identification of functional RNA candidates based on user-defined criteria.

    Conclusions:

    • fRNAdb serves as a valuable computational resource for non-coding RNA research.
    • The database aids in prioritizing transcripts for further experimental investigation.
    • Computational mining reduces the cost and effort associated with identifying functional non-coding RNAs.