# Defining Blood Plasma and Serum Metabolome by GC-MS
## 5 Workflow of GC-MS Analysis of Blood Metabolome
### Data Processing  
Metabolomics is a data-intensive scientific field. The raw data acquired by tandem chromatography with mass spectrometry are a complex three- (or even four in case of GC\(\times\)GC) dimensional set of retention times, \(m/z\) values, and their intensities. Interpretation of results obtained in a GC-MS experiment is a delicate process. Each academic group independently chooses to use commercial or freely available software or even create customized scripts. Commercial software is mature and user-friendly, usually with a developed graphical interface. The vendor supplies such packages along with the equipment, i.a. ChemStation by Agilent Technologies [172], MassLynx by Waters Corporation [173], ChromaTOF by Leco Corporation [174], Compound Discoverer by Thermo Scientific [175], etc. In accordance with text mining results, commercial software accounts for about 38% of computational solutions used for GC-MS data processing [176]. Conversion of the raw data to an open standard format such as mzML allows subsequent processing via vendor-independent software. The most remarkable example of public software is AMDIS [177]. For more than 20 years of its existence, AMDIS has been \"enriched\" with various extensions improving its operation [178,179]. In toxicological \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mass** & **Resolution** & **Mass Range (Da)** & **Acquisition Speed** & **Major Benefits** & **Major Limitations** \\ \hline Quadrupole & \(\sim\)1000 & 50–6000 & Medium & Highly selective & Low resolution \\  & & & & Well suited for pairing with GC & Narrow mass range \\  & & & & Relatively cheap & Compact & Narrow mass range \\ \hline Ion trap & \(\sim\)1000 & 50–4000 & Medium & Compact & Narrow dynamic range \\  & & & & Relatively cheap & Limited resolution \\  & & & & Highly sensitive & Requires pulsed \\  & & & & & introduction to MS \\ \hline FT-ICR & over 1,000,000 & 10–10,000 & Slow & High resolutive & Expensive and bulky \\  & & & & High reproducibility & Slow scanning \\  & & & & High resolving power & Specific coupling with \\  & & & & Wide dynamic range & chromatography systems \\ \hline Orbitrap & up to 240,000 & 40–4000 & Slow & High resolution & Narrow mass range \\  & & & & Compact and elegant solution & \\ \hline TOF & up to 60,000 & 20–500,000 & Fast & Highly sensitive & Requires pulsed \\  & & & & Wide mass range & introduction to MS \\  & & & & Fast scanning & Requires fast solutions for \\  & & & & Well suited for pairing with GC & data acquisition \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of popular mass analyzers used in plasma and serum metabolomics. studies of serum samples, automatic evaluation of GC-MS data using AMDIS and its extensions Maurer/Pfleger/Weber identified additional drugs in 17% of samples that had been ignored by experienced personnel during manual data curation [180]. MetaboliteDetector [181], MetaboAnalyst [182], XCMS [183], metaMS [184], MetAlign [185], and MZmine [186] also are successful examples of public software for GC-MS data processing [187]. It is important to note that in data processing, there is a visible trend to shift to online platforms for data analysis, which opens up opportunities for data analysis with only internet access required. Special attention should be paid to the linkage between XCMS and MetaboAnalyst, which allows the researcher to perform a full cycle of metabolomic data analysis: from pre-processing to enrichment analysis, mapping the identified small molecules to metabolic pathways, etc. Both public and commercial packages for GC-MS data processing perform primary preparation of raw files (noise smoothing, baseline correction, feature detection, alignment, normalization), library matching, visualization, and, optionally, downstream analysis (Figure 6). After carrying out primary processing, allowing to organize the data and check their integrity, one can extract useful information from the cleaned dataset.