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Researchers analyzed microRNA (miRNA) databases, finding recent versions differ due to new sequencing. They developed a method to identify novel blood-borne miRNAs, validating 518 candidates.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Small non-coding RNAs, including microRNAs (miRNAs), are crucial regulators in biological processes.
  • The miRBase database has cataloged miRNA sequences since 2004, with ongoing updates.
  • Advancements in sequencing technologies have influenced miRNA discovery and database content.

Purpose of the Study:

  • To investigate sequence and structural characteristics of annotated miRNAs across miRBase versions.
  • To identify key features for predicting novel miRNA candidates.
  • To discover and validate novel blood-borne miRNA candidates.

Main Methods:

  • Comparative analysis of miRNA sequences and structures in miRBase versions.
  • Development of a predictive algorithm based on miRBase characteristics.
  • High-throughput sequencing of blood cells (705 samples, 9.7 billion reads).
  • Bioinformatic prediction of novel miRNAs using miRDeep2.
  • Validation of candidate miRNAs using qRT-PCR and northern blotting.

Main Results:

  • Recent miRBase versions (v20, v21) show significant deviations from earlier versions, influenced by next-generation sequencing.
  • A predictive algorithm identified 518 novel blood-borne miRNA candidates after filtering.
  • Novel candidates were successfully validated, confirming their biological relevance.
  • A web server (www.ccb.uni-saarland.de/novomirank) was developed for ranking potential miRNA candidates.

Conclusions:

  • Next-generation sequencing has altered miRNA profiles in databases.
  • The developed method effectively predicts and identifies novel blood-borne miRNAs.
  • This work provides a valuable resource for miRNA research and diagnostics.