November 29-30, 2018
Hilton San Francisco Financial District
San Francisco, CA
Erin Baker, Pacific Northwest National Laboratory
Gary Patti, Washington University at St. Louis
With recent advances in instrumentation, it has become routine to acquire high-quality LC/MS-based metabolomic data. Accordingly, the field has grown exponentially and the number of service facilities offering metabolomic services continues to rise. Indeed, the technology is now readily available to most interested researchers at relatively affordable costs around the world. Following the path of its ‘omic predecessors, metabolomics is now in high demand among both technological specialists as well as biologists and clinicians who see the power of its application.
Despite the increasing amount of untargeted metabolomic data being acquired, the ability to process and interpret the data is still severely limited. It is typical to detect thousands of metabolomic features in LC/MS experiments performed on biological samples. Yet, at this time, only a small fraction of these features can typically be identified with biochemical names. Moreover, in most cases, the process of going from raw metabolomic data to biochemical structures is not automated. The informatic burden can require days, weeks, or even months of time and resources. Even then, after extensive data analysis, there may be large numbers of “unknowns” that cannot be characterized. Thus, although many researchers now have access to metabolomic data, the challenge of interpreting the results has created major obstacles that are preventing the full potential of metabolomics from being realized.
The ASMS Fall Workshop “Metabolomics Informatics” is designed to provide a broad perspective on the state of data processing in metabolomics by leading experts in the field. The goal is not only to make attendees aware of the most state-of-the-art informatic resources, but also to educate investigators on proper methods for reliably interpreting datasets. Processing of metabolomic data involves multiples steps, ranging from peak detection to database searching and pathway mapping. Scientists who have created foundational resources to accomplish these various steps will present their contributions (highlighting latest advances, tips, and tricks) to help attendees maximize the value of their metabolomic data and hopefully prevent misinterpretation.
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