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
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2. Excise sequence from genome using not discarded reads
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3. Predict secondary structure and discard unlikely ones
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Core algorithm
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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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