Streamlining GC-MS metabolomic analysis using the M-IOLITE software suite

Metabolomics, as a rapidly growing omic analysis; has being used extensively to explore the dynamic response of biological systems in several diseases/disorders and contexts. Therefore; it has become commonplace in a wide variety of disciplines and there is an intense need for development of software suites that provide the user with a less complicated and invalid analysis. These suites must integrate meta-analysis, a standardized data normalization method and a safe repository for all types of biological samples. In the case of the Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics, due to the complexity of the analysis; multiple procedures that are essential for the metabolite identification require special manipulation. Moreover, metabolomic analysis produces a vast amount of unidentified compound data, so there is a need for unknown peak identification methods. While a number of tools offer access to datasets, constantly providing 11C releases for data processing and the fact that considerable progress has been made in that area, there is no computational platform that emerges as a standardized approach which includes specialized normalization methods for GC-MS metabolomic analysis and incorporates the metabolic network analysis into data interpretation and unknown peak identification. To address these issues, as the datasets obtained from metabolomics experiments still remain extremely large and dense, we have implemented M-IOLITE, a computational suite for the efficient and automatic analysis of high-throughput metabolomic experiments. The aim of the suite is to streamline GC-MS metabolomic data analysis and to reduce complexity enabling the use of a friendly interface for processing, validating and annotating data. It integrates specialized normalization methods, a safe data repository and a peak library providing through its pipeline a useful tool which enables rapid and accurate analysis of the metabolomic profiles into an interactive system. (C) 2016 IFAC (International Federation Of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.