AIoutput

Program download

If you use 32 bit Windows OS and Excel 2007/2010, please download the macro file, example file, and manual: AIoutput2 ver. 1.30.
If you use 64 bit Windows OS and Excel 2010, please download AIoutput ver. 1.30 for 64 bit and Excel 2010.

Recently, we determined to use a fork version of AIoutput program for Shimadzu GC-MS data sets.
If the instrument, Shimadzu GCMS 2010 Ultra, is used in your laboratory, the fork version should be recommended.
AIoutput 32 bit for Shimadzu.
AIoutput 64 bit for Shimadzu.

License: Creative-Commons By-Attribution (CC-BY)

The GC/MS library and its analytical conditions are available below:
1. Pegasus III TOF-MS system, LECO; GC 6890, Agilent Technologies; Column CP-SIL 8 CB LOW BLEED/MS
2. GCMS-2010 Plus, Shimadzu; Column InertCap 5MS/NP
License: Creative-Commons By-Attribution (CC-BY)

Objective

GC/MS is one of the most popular platforms for comprehensive analysis of metabolites in living organisms. The crucial process is to construct an organized two-dimensional data matrix containing compound names and their quantitative values. Because this process is the most complicated and knowledge intensive task in GC/MS-based metabolomics, it is essential to develop a tool for accurate, automatic data processing. We used the MetAlign (Lommen, 2009) data pre-processing tool. AIoutput can perform the peak identification, prediction, and data integration from the result exported from MetAlign and user defined retention time and spectra library. AIoutput is a non-targeted and targeted analysis tool for GC/MS based metabolomics written in visual basic for application (VBA, excel macro) available in Microsoft Excel Windows versions 2007 and later.

Please cite

  • Practical non-targeted gas chromatography/mass spectrometry-based metabolomics platform for metabolic phenotype analysis. J. Biosci. Bioeng. 112, 292-298, 2011 [PubMed]
  • GC/MS based metabolomics: dvelopment of a data mining system for metabolite identification by using soft independent modeling of class analogy (SIMCA). BMC Bioinformatics 12: 131, 2011 [PubMed]

Overview of AIoutput software

Data pre-processing
We use MetAlign software for data preprocessing of GC/MS raw data. MetAlign offers the data matrix aligned with the RT and m/z without missing values by performing the noise estimation, the binomial based digital filter (for smoothing), the peak estimation program (for peak detection), and the peak alignment program based on the amplitude and retention time.

GC/MS library
AIoutput identifies the detected peaks on the basis of the reference retention time and mass spectra included in the user defined GC/MS library. Both the retentiom time (RT) and retention index (RI) based on n-alkane mixture is available as index.

At 2014/2/1, we offer two type of GC/MS libraries. Both libraries stores the retention time and mass spectra of the derivative metabolites by methoxyamine and MSTFA.

1. Pegasus III TOF-MS system, LECO; GC 6890, Agilent Technologies; Column CP-SIL 8 CB LOW BLEED/MS (Cotributor Osaka Univ., Japan)
2. GCMS-2010 Plus, Shimadzu; Column InertCap 5MS/NP (Contributor GL Science, Japan; Copyright GLS)

The detail of analytical condition is downloadable together with the library.

Data processing
AIoutput can integrate the MetAlign result to the mass spectra tags (MSTs), identify the MSTs on the basis of the retention time similarity (based on gaussian function) and mass spectra (based on correlation coefficient) with the reference, and predict unknown peaks by mulitivariate analysis model (based on soft independent modeling of class analogy).

Statistical analysis
Upon constructing the matrix, AIoutput can normalize the variables by the internal standard and perform further statistical and graphical analyses. The software can visualize the bar or line chart of each metabolite, or perform principal component analysis, projection to latent structure (PLS), PLS discriminant analysis (PLS-DA) and statistical tests.
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