discovering micrornas from deep sequencing data using ksung/gs5002_2012/2nd_present/ discovering...

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  • Discovering microRNAs from deep sequencing data using miRDeep

    Lin Min

  • miRNA

  • 1. Map to genome and discard reads • Map to many loci • Map to rRNA tRNA

    Flow Chart

  • 2. Excise sequence from genome using not discarded reads

    Flow Chart

  • 3. Predict secondary structure and discard unlikely ones

    Flow Chart

  • Core algorithm

    Flow Chart

  • Features: • Reads signature

    Core Algorithm

    Find features to distinguish miRNA from noise

  • Features: • Reads signature • Structural stability

    Core Algorithm

    Find features to distinguish miRNA from noise

  • Core algorithm

    Features: • Reads signature • Structural stability • 3’ end overhang

    Core Algorithm

    Find features to distinguish miRNA from noise

  • Features: • Reads signature • Structural stability • 3’ end overhang • 5’ end conservation

    Core Algorithm

    Find features to distinguish miRNA from noise

  • Core algorithm

    Features: • Reads signature • Structural stability • 3’ end overhang • 5’ end conservation

    Pre/bgr

    Naïve Bayesian Model

  • C. elegans.

    116 Passed Cut off 103 previously known 13 new

    5 are verified by northern 4 out of them are detected

    Results

  • Human

    173 Passed Cut off 163 previously known (28%) 10 new

    Results

  • 206 Passed Cut off 3 previously known 203 new

    Dog

    Results

  • Thank you

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