Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis

Abstract

Biological systems are increasingly being studied in a holistic manner, using omics approaches, to provide quantitative and qualitative descriptions of the diverse collection of cellular components. Among the omics approaches, metabolomics, which deals with the quantitative global profiling of small molecules or metabolites, is being used extensively to explore the dynamic response of living systems, such as organelles, cells, tissues, organs and whole organisms, under diverse physiological and pathological conditions. This technology is now used routinely in a number of applications, including basic and clinical research, agriculture, microbiology, food science, nutrition, pharmaceutical research, environmental science and the development of biofuels. Of the multiple analytical platforms available to perform such analyses, nuclear magnetic resonance and mass spectrometry have come to dominate, owing to the high resolution and large datasets that can be generated with these techniques. The large multidimensional datasets that result from such studies must be processed and analyzed to render this data meaningful. Thus, bioinformatics tools are essential for the efficient processing of huge datasets, the characterization of the detected signals, and to align multiple datasets and their features. This paper provides a state-of-the-art overview of the data processing tools available, and reviews a collection of recent reports on the topic. Data conversion, pre-processing, alignment, normalization and statistical analysis are introduced, with their advantages and disadvantages, and comparisons are made to guide the reader.

Direct infussion Electrospray Mass Spectrometry as a new non-invasive tool for serum metabolomics in induced-stress subjects

“…The models gave rise to a good classification outcome as shown in the score plots (Figure 2). These groups of results were evaluated statistically and the resulting models showed values in line with quality parameters R 2 and Q 2 (explained variance of approximately 99% and a predicted variance above 50%) 53 . T-test analysis with p-values and data modelling using the PLS (Principal Least Squares) progression were carried out 54,55 .…”

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

“…Raw LC-MS data must first be translated into lists of metabolites with one of a number of software tools (Castillo et al 2011;Sugimoto et al 2012). Our data processing protocol employs the free MZmine 2 framework for MS data visualization and analysis (Pluskal et al 2010a) (see Protocol: Measurement of Metabolome Samples Using Liquid Chromatography-Mass Spectrometry, Data Acquisition, and Processing and Fig.…”

Metabolomics applied in bioenergy

“…For comparison of biological groups (e.g., control and treated samples, mutant and wild type), a wealth of statistical and machine learning algorithms using unsupervised (e.g., hierarchical clustering and principal component analysis) or supervised (e.g., ANOVA, partial least squares) methods enable comprehensive identification of variables (metabolic features) in order to capture the dimension of variation among the entire dataset ( Figure 1E) [69,70,74,80,81]. After this, data visualization tools allow for the simplification and incorporation of metabolic data into biochemical pathways, facilitating interpretation ( Figure 1E).…”