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Institute for Translational Medicine and Therapeutics

Director: FitzGerald, Garret, M.D.


The Institute for Translational Medicine and Therapeutics (ITMAT) supports research at the interface of basic and clinical research focusing on developing new and safer medicines. ITMAT includes faculty, basic research space, and the Clinical and Translational Research Center (CTRC), which now includes the former General Clinical Research Center (GCRC) of both Penn and the Children's Hospital of Philadelphia (CHOP). ITMAT also offers research cores, educational programs (including a Masters in Translational Research), and research centers.





  • Research Facilitator service ( Support service )

    "The Research Facilitator Program is the single point of contact for all clinical and basic science research related questions for study start-up, study conduct, and throughout the duration of a project. Whether submitting a grant application, preparing to implement a study, looking for resources to use in your study (core lab services, model organisms, and more!) or just wanting to brainstorm about a study idea, it is important to contact the Research Facilitator Program early.

    The ITMAT Research Facilitator Program has a team of knowledgeable professionals that can respond to any research related question from basic protocol design, resources available within ITMAT and the local research community, biostatistics, budgeting and the IRB application process.

    The ITMAT Research Facilitator Program also connects researchers with all research resources and facilitates inter-institutional interactions among the Children's Hospital of Philadelphia, the Wistar Institute, the Monell Institute, and the University of the Sciences in Philadelphia by providing excellent, efficient, and experienced assistance to investigators and research staff."


  • Empirical Bayes Protein identifier ( Algorithmic software suite )

    "EBP calculates protein expression probabilities based on peptide sequence identifications from search algorithms such as Mascot and Sequest. Protein lists can be generated by choosing proteins whose expression probabilities exceed a threshold value. Varying the probability threshold allows the sensitivity of protein identification to be balanced against the false positive error rate.

    The statistical model assumes that every peptide sequence that could theoretically result from enzymatic digestion of a protein in the search database has a chance of being identified in the search results, whether correctly or incorrectly. The probabilities of correct identification are combined across multiple peptide searches using a function that returns the maximum probability from consensus identifications, and penalizes non-consensual identifications.

    Both correct and incorrect peptide sequence identifications are assumed to occur at random in this "space" of peptides, at rates that are governed by model parameters including protein length, estimated protein abundance, the size of the search database, and the number of peptide sequence identifications in the dataset. Degenerate peptides whose sequence matches multiple proteins are treated using "Occam's Razor", a principle by which the smallest set of probable proteins is chosen that is sufficient to explain the peptide sequence identifications.

    For each protein in the database, a likelihood ratio is calculated for the possibility that the peptide identifications whose sequence matches the protein are all incorrect. These likelihood ratios are used to estimate the expression probabilities, from which updated parameter estimates are obtained. The procedure is iterated until the algorithm converges at the maximum likelihood estimates.

    Replicated datasets can be analyzed by estimating multiple sets of model parameters can be estimated simultaneously. In this way, hypotheses about protein expression can be tested using the results of replicate experiments."

    "EBP can be run as an alternative to ProteinProphet as part of the Sashimi Trans-Proteomic Pipeline."

  • Waveclock ( Algorithmic software component )

    "Waveclock is an R function designed to assess the period and amplitude of cycling cell luminescence data. The function reconstructs the modal frequencies from a continuous wavelet decomposition of the luminescence data using the ’crazy climbers’ algorithm described in ‘‘Practical Time-Frequency Analysis: Gabor and Wavelet Transforms with an Implementation in S’’, by Rene Carmona, Wen L. Hwang and Bruno Torresani, Academic Press, 1998."

    Waveclock can be used to analyze other physiological measurement data, such as blood pressure and gene expression data.

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Last updated: 2019-11-11T15:13:20.822-05:00

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