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Opportunity at National Institute of Standards and Technology (NIST)

Differentiation of Analytical and Biological Variability in Metabolomics using Standard Reference Materials and Multivariate Statistics

Location

Material Measurement Laboratory, Biomolecular Measurement Division

RO# Location
50.64.51.B7843 Gaithersburg, MD

Please note: This Agency only participates in the February and August reviews.

Advisers

Name E-mail Phone
Simon, Yamil ysimon@nist.gov 301.975.8638

Description

NIST has long developed and provided reference materials to assist others in making reliable measurements. The NIST Standard Reference Materials (SRMs) include several complex biological materials, especially human plasma and urine. These materials are particularly useful to distinguish between biological variability and variability that is due to experimental conditions in metabolomics experiments. The aim of this project is to establish a standard protocol to evaluate the analytical variability associated with liquid chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a considerable amount of data of the standard biological fluids on different LC-MS platforms and in-house software has been developed for chromatography alignment, spectral similarity, peak deconvolution, and molecular feature extraction. The successful applicant will be expected to work closely with experimentalists to computationally implement multivariate statistical techniques to evaluate the reproducibility of LC-MS patterns of biological fluids. Previous knowledge and experience of a high-level programming language--preferably C++, Python, or Java and of pattern recognition techniques--to draw statistically sound conclusions, are necessary.

 

References

Broadhurst DI; Kell DB: Metabolomics 2(4): 171, 2006

Yamil Simón-Manso, et al: Analytical Chemistry, DOI: 10.1021/ac402503m

 

Keywords:
Metabolomics; Pattern recognition; Multivariate statistics; LC-MS analysis; Computer programming; Data analysis; Chemometrics; Principal component analysis; D-partial least squares;

Eligibility

Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants
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