Metabolomic data analysis: Solutions and challenges ahead

Metabolomic data analysis: Solutions and challenges ahead

November 29, 2016

8:00 a.m. PST / 11:00 a.m. EST / 16:00 GMT / 17:00 CET

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Metabolomics presents unique challenges in terms of data acquisition, data processing, data standardization, statistical analyses, and identification of unknowns. Robust and precise analytical methods are essential to metabolomics, but data acquisition is only part of the challenge. It is equally important to develop both software tools and appropriate workflows to extract meaningful data and generate results from metabolomics datasets. 

The novel cmPALS This webinar provides an overview of approaches and software post-data acquisition workflows, including novel tools being used in the detection of statistically significant patterns and identification of underlying compounds and their pathway context. Finally, we will explore current approaches in integrating metabolomic data with other multi-omic datasets.
and the new Omega Cuvette enable faster and more sensitive Zeta Potential measurements that facilitate ease of use and improve sample stability. Additionally, the transmittance, or amount of light traveling through the sample, is constantly monitored to provide additional information about the sample turbidity and stability. Finally, improvements have been made to the particle size distribution algorithms to enable the resolution of bi- and tri-model particle mixtures.

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Participants Will Learn:


• Understand necessary components of a MS-based successful Metabolomics data analysis experiment

• Understand the bottlenecks of metabolite compound identification and annotation in LC-MS data and the strategies to address them

• Discover software tools that mine rich MS data to make biological inferences

• Integrate robust Metabolomic data sets to multi-Omics related studies

Who Should Attend:


• Researchers handling rich biological data and challenging sample types

• Researchers performing large, disease-related profiling studies

• Mass spectrometrists needing to extrapolate new Metabolomic data to old studies



Julian Avila-Pacheco, Ph.D.,
Research Scientist,
Broad Institute


Kevin Davies,

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