MiRWoods: Enhanced precursor detection and stacked random forests for the sensitive detection of microRNAs.

ABSTRACT: MicroRNAs are conserved, endogenous small RNAs with critical post-transcriptional regulatory functions throughout eukaryota, including prominent roles in development and disease. Despite much effort, microRNA annotations still contain errors and are incomplete due especially to challenges related to identifying valid miRs that have small numbers of reads, to properly locating hairpin precursors and to balancing precision and recall. Here, we present miRWoods, which solves these challenges using a duplex-focused precursor detection method and stacked random forests with specialized layers to detect mature and precursor microRNAs, and has been tuned to optimize the harmonic mean of precision and recall. We trained and tuned our discovery pipeline on data sets from the well-annotated human ge

SUBMITTER: Bell J 

PROVIDER: S-EPMC6785219 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature