eagle-i University of PennsylvaniaUniversity of Pennsylvania
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Hogenesch Laboratory

Summary:

Our laboratory studies the mammalian circadian clock using genomic and computational tools. We use these tools to discover new clock genes, learn how the clock keeps time, and how it coordinates rhythms in physiology and behavior. This clock research drives development of genomic and computational methods that we apply to other areas of biology. Finally, we recognize biological complexity and conduct this research at the network, rather than single gene, level.

Affiliations:

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Databases

  • CircaDB ( Database )

    "Circadian rhythms drive daily oscillations in many different physiologies, including blood pressure, body temperature and behavior. In addition, disruptions of the circadian clock contribute to the pathologies of metabolic disorders as well as cardiovascular disease and cancer. Our lab is interested in the mechanism by which circadian oscillations drive these processes. We are using microarray analysis in combination with curve-fitting and Fourier-transform based statistical algorithms to search the genome for transcripts which oscillate under circadian control. We have identified more than 3000 cycling genes in the liver and smaller subsets of rhythmic genes in the pituitary and fibroblasts. We are currently expanding our initial studies to identify rhythmic transcripts in over 15 different tissue types including the heart, kidneys and brain. These data sets will be mined using bioinformatic techniques to discover novel circadian regulatory elements, characterize components of the circadian clock, and identify regulatory genes responsible for the physiologies and pathologies associated with circadian rhythms."

Software

  • JTK_Cycle ( Algorithmic software component )

    "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."


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Last updated: 2013-04-02T12:33:31.516-04:00

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