Towards Realistic Benchmarks for MicroRNA Precursor Discovery Algorithms
Date Issued
2010
Date
2010
Author(s)
Huang, Chun-Yi
Abstract
MicroRNAs (miRNAs) are short non-coding RNAs (~21 – 23 nucleotides) participating in post-transcriptional regulation of gene expression. There have been many efforts on discovering miRNA precursors (pre-miRNA) over the years. Recently, ab initio approaches get more attention compared to comparative approaches because of ab initio discard sequence alignment and can discover species-specific pre-miRNAs.
Because to systematically identify miRNAs from a genome by existing experimental techniques is difficult, the use of computational methods is a key factor in miRNA discovery , However, the success of ab initial approach has not been well evaluated and extended to genome-wide miRNA discovery.
In this study, a systematic analysis is performed to figure out the theoretic sampling rate that makes the evaluation statistically significant. Furthermore, we proposed a approach to reduce the negative set, and successfully generate a compact set which is smaller than the theoretic size but can yield accurate performance evaluation. Considering that there are some prevailing negative sets, this study also proposes a mathematic model that can estimate the realistic performance based on those obtained with biased datasets. Finally , 5 pre-miRNA predictors are re-evaluated based on the proposed benchmarks. The experimental results show that the proposed benchmarks can helps researchers to realize and compare the realistic performance of alternative methods.
Subjects
microRNA
whole genome scan
systematic sampling
predictor
performance benchmark
Type
thesis
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