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JTK_Cycle

eagle-i ID

http://eagle-i.itmat.upenn.edu/i/0000013a-3250-0720-01af-beb880000000

Resource Type

  1. Algorithmic software component

Properties

  1. Resource Description
    "Successful circadian analysis of microarray datasets requires powerful and specific statistical tests to identify cycling genes in noisy datasets as well as accurate and precise statistical measures to determine crucial attributes of their rhythms including period, phase, and amplitude. JTK_Cycle is a novel non-parametric statistical algorithm designed to identify and characterize cycling variables in large datasets. As compared with COSOPT and Fisher's G test, JTK_Cycle successfully identifies more rhythmic transcripts with fewer false positive observations."
  2. Contact
    Hogenesch, John B., Ph.D.
  3. Used by
    Hogenesch Laboratory
  4. Data Input
    Microarray data
  5. Data Output
    Circadian parameters
  6. Software purpose
    Data analysis objective
  7. Related Publication or Documentation
    JTK_CYCLE: an efficient non-parametric algorithm for detecting rhythmic components in genome-scale datasets
  8. Related Publication or Documentation
    Interaction between circadian clocks and metabolic physiology: implications for reproductive fitness
  9. Related Publication or Documentation
    A circadian gene expression atlas in mammals: Implications for biology and medicine
  10. Website(s)
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3119870/#R7
  11. Related Technique
    Nucleic acid microarray assay
  12. Related Technique
    Transcription profiling
  13. Algorithm used
    Jonckheere–Terpstra test
  14. Algorithm used
    Kendall's tau coefficient
  15. Algorithm used
    Harding algorithm
  16. Coded in
    R language
 
RDFRDF
 
Provenance Metadata About This Resource Record
  1. workflow state
    Published
  2. contributor
    fcoldren
  3. created
    2012-10-05T14:13:50.077-05:00
  4. creator
    fcoldren
  5. modified
    2014-10-29T10:53:10.796-04:00

Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016