Development of Novel Computational Methodologies for Analyzing Post-Processed Untargeted LC-MS Metabolomics Data
Mak, Tytus D
In recent years, the field of metabolomics has quickly risen to become a crucial platform for conducting biological research. Its unique capabilities, which provides unparalleled comprehensive quantitative insight into the constituents of metabolic processes, makes the platform a potentially transformative technology. However, it is these novel aspects, coupled with the immaturity of the techniques utilized, which has contributed to the many difficulties faced by pioneers in the field. Nonetheless, its potential for becoming the bedrock technology for gaining a deeper understanding into the metabolic processes behind many biological diseases underscores the importance of advancing the metabolomics platform. A key aspect in advancing the platform is the development of specialized tools for processing the substantial and often cryptic quantitative data that is generated. The sheer volume of data produced, coupled with the specialized knowledge and expertise necessary to understand it, emphasizes the importance in creating automated high throughput tools that enable streamlined analysis and produces meaningful results.Development of tools for post-processing, which is comprised of the computational methodologies for extracting statistically and biologically relevant conclusions from post-processed metabolomics data, is crucial in cultivating metabolomics into a more complete and mature platform. In pursuit of these objectives, we have developed three computational methodologies that attempt to take advantage of the strengths of metabolomics data sets while at the same time accounting for its shortcomings. Initial efforts resulted in the creation of the Visual Analysis of Metabolomics Package (VAMP), a tool that allows for all small molecule components in the metabolome to be holistically visualized and qualitatively evaluated. The necessity for a more quantitative approach led to the development of MetaboLyzer, an analysis workflow that incorporates many classical univariate and multivariate biostatistical approaches. Finally, an attempt to make a truly novel algorithm for the express purpose of analyzing metabolomics data resulted in Selective Paired Ion contrast Analysis (SPICA), a methodology that relies on analyzing ion-pairs rather than single-ions, which affords numerous advantages in minimizing normalization issues and noise reduction. Taken together, these efforts represent what is hopefully a step forward in evolving the field of metabolomics.
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