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RIQC-RankFilter GC-MS from tabular file (version 1.0.2)

Sample File:

Select a tab delimited NIST metabolite identifications file (converted from PDF)

Calibration File:

Calibration file containing reference masses (e.g. alkanes) with their respective RT and RI values

Analysis Type:

Select the type of analysis that has been used to generate the sample file

Select a model to be used :

Both linear and (3rd degree) polynomial models are available

Library:

Reference global lookup library file with CAS numbers and respective (previously calculated) RIsvr values

Window:

Basically estimates the experimental RI (RIexp) by building a RI(RT) function based on the given calibration file.

It also determines the estimated RI (RIsvr) by looking up for each entry of the given input file (Sample File), based on its CAS number, its respective RIsvr value in the given global lookup library (this step is also called the "RankFilter analysis" -see reference below; Sample File may be either from NIST or AMDIS). This generates an prediction of the RI for a compound according to the "RankFilter procedure" (RIsvr).

Output is a tab separated file in which four columns are added:

Rank Calculated rank

RIexp Experimental Retention Index (RI)

RIsvr Calculated RI based on support vector regression (SVR)

%rel.err Relative RI error (%rel.error = 100 * (RISVR − RIexp) / RIexp)

Notes

The layout of the Calibration file should include the following columns: 'MW', 'R.T.' and 'RI'.

Selecting 'Polynomial' in the model parameter will calculate a 3rd degree polynomial model that will be used to convert from XXXX to YYYY.

References

RankFilter: Mihaleva et. al. (2009) Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index. Bioinformatics, 25 (2009), pp. 787–794