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eagle-i ID


Resource Type

  1. Software


  1. Resource Description
    "CoRAL is a machine learning package that can predict the precursor class of small RNAs present in a high-throughput RNA-sequencing dataset. In addition to classification, it also produces information about the features that are most important for discriminating different populations of small non-coding RNAs."
  2. Additional Name
    Classification of RNAs by Analysis of Length
  3. Used by
    Wang Laboratory
  4. Operating System
  5. Data Input
    bam file containing small RNA seq data
  6. Data Input
    Genome sequence (only needed for MFE computation)
  7. Data Input
    Annotation package from CoRAL site
  8. Data Output
    Data matrix containing all locus and feature data
  9. Data Output
    Known classes based on the annotation
  10. Data Output
    Individual feature data
  11. Data Output
    Locus annotation data
  12. Data Output
    Called loci; chr,start,end,locus_id,read_count,strand
  13. Data Output
    Run file: class-wise recall and ppv
  14. Data Output
    Run file: number of loci in each class
  15. Data Output
    Run file: difference in mean value of features within one class vs others
  16. Data Output
    Run file: number of times features were selected by varSelRF for each class
  17. Data Output
    Run file: total performance for multi-class classifier
  18. Data Output
    Run file: description of the parameters used for run
  19. Data Output
    Run file: the trained model (data.Rdata)
  20. Software purpose
    Nucleic acid sequence feature identification objective
  21. Related Publication or Documentation
    CoRAL: Predicting non-coding RNAs from small RNA-sequencing
  22. Related Publication or Documentation
    Using machine learning and high-throughput RNA sequencing to classify the precursors of small non-coding RNAs
  23. Website(s)
  24. Related Technique
    RNA sequencing
  25. Software license
    Academic software license
  26. Algorithm used
    Random forest
  27. Coded in
    R language
  28. Coded in
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Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016