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Retention Time Alignment

Major steps followed by the Retention Time algorithm

Collect species from the reference User context and predict their retention time using an external utility
For each identification gathered under the reference User context do:
1. Collect species to align from the identification
2. Constitute several pair groups between collected reference species & species to align. A pair group contains 2 groups of species having same sequence & calculated mass (group1 has species from reference User context and group2 has species to align from identifications).
3. Compute one (or several) representative value(s) for group1 & group2 for each pair group
4. Compute linear regression between representative values
5. Store linear regression coefficients

Species Retention Time of the reference UserContext are predicted with NETPrediction v2.2.3378 utility using Kangas method (click here for more details). NETPrediction utility only uses species sequences to predict a Normalized Elution Time (NET) value. First, a list of 'reference' species is built with the below criteria, then the corresponding sequences are exported in order to be used by the NETPrediction utility:
- The reference species list doesn't contain any species with PTMs
- If several species exist with redundant sequences, the best score species is retained
The predicted NET are converted to retention time. The user can decide to exclude predicted values too far from the others. It is actually based on
and stored as properties in the reference species.
For each identification existing under the reference User context:
1. All the final child species to align are collected
2. Several pair groups are created using reference species & species to align. A pair group contains 2 groups of species having same sequence & calculated mass (group1 has species from reference User context and group2 has species to align from identifications).