# Analyte identifications

isotopic cluster-based deconvolution low mass deconvolution deconvoluted feature feature-identity dictionary

After a successful deconvolution step, nothing is more natural than to try to get help identifying the analytes under the mass peaks. Because the general process leading to analyte identification is rooted in the deconvolution of the input mass spectrum, the configuration of the analyte identification is started by checking the Check to perform analyte identification check box as shown in .

Checking the box automatically displays, in the same application preferences window, the Analyte Identification page, as in .

Configuration of the analyte identification

The analyte identification process is performed right after the deconvolution process and uses a dictionary that maps neutral chemical formulas with analyte identities.

The format of the dictionary file is CSV, with the field separator set to TAB (also known as the TSV format) and looks like the following. The compulsory header line must contain at least the there column headers "Id", "Structure" and "Formula". The "Id" column will be used to annotate the spectra.

    Id
    Structure
    Formula
    Gly
    Gly
    C2H5N1O2
    Ala
    Ala
    C3H7N1O2
    Glu
    Glu
    C5H9N1O4
    DAP
    DAP
    C7H14N2O4
    DAP-Ala -> Glu-DAP
    DAP-Ala -> Glu-DAP
    C22H38N6O11

The formulas listed in the dictionary file must be for neutral analytes.

The reason why the isotopic data file path name is required is that each elemental composition of the dictionary is used to compute the mass of the analyte. This is useful, as shown in the figure, because the analytes could have been fully labelled with stable isotopes of any chemical element entering in their chemical composition.

A general overview of the centroid extraction→deconvolution→analyte identification workflow is illustrated at .

Worflow leading from a mass spectrum to analyte identity suggestions

Starting from a mass spectrum, it is possible to extract centroids, perform the deconvolution based on these, create a synthetic mass spectrum of the deconvoluted features and finally suggest analyte identities for these.

The figure illustrates the various steps in the workflow:

- The original mass data are integrated to a mass spectrum.

- The centroid extraction from the original input mass spectrum (1; see ) produces a bar graph plot (blue, 2) and a list of centroids printed to the console window (3).

- The centroids are used to perform the deconvolution (see , which produces a list of deconvoluted features (6; neutral masses); each deconvoluted feature lists so-called supporting ions that correspond to the one or more &mz; values that have been found to correspond to different ionization levels of that neutral feature. The signal intensity for a given deconvoluted feature corresponds to the sum of the intensities of the monoisotopic centroids in the clusters of the different supporting ions. Each supporting ion carries with itself the calculated (theoretically expected) isotopic cluster that was found to match the observed isotopic cluster for it (with calculated cluster centroid intensities normalized to the intensity of the observed monoisotopic centroid). That isotopic cluster is then leveraged in case the user requires a synthetic mass spectrum (5) that simulates a mass spectrum containing all of the deconvoluted features.

  One major interest in the crafting of the synthetic mass spectrum is to very easily check which original input centroided mass spectrum's features did actually convert into deconvoluted features. This is visible in the figure, where, in the synthetic mass spectrum (5) one isotopic cluster only was successfully deconvoluted, while the input mass spectrum (4) had three isotopic clusters.

  The list of deconvoluted features is output in the console window (6).

- If the user requires help in the identification of the analytes below the deconvoluted features, they may provide a dictionary that maps analyte identifications to analyte elemental compositions (in neutral form). In that case, the software goes on and tries to map the deconvoluted features' neutral masses to the masses in the dictionary. The lower pane of the figure shows one deconvoluted feature (11) that was successfully matched to two analyte structural hypotheses.

  The left pane (9) shows the input mass spectrum (upper graph) annotated with two structural hypotheses pointing to a twice-charged isotopic cluster (z=2) that is one of the two supporting ions of the deconvoluted feature (11). The lower graph shows that in the &mz; region, there were two deconvoluted features, but only one could be annotated. The right pane (10), shows the annotation for the supporting ion that is charged once (z=1).

Deconvolutions are performed in order to get back to the Mr mass of the analyte while reading &mz; values. In the following sections, all the avaiable deconvolution processes are described.

Before delving into the deconvolutions, it is necessary to present two menu options that are found in the plot widgets contained in the Mass spectra window: the menu items under the Centroidation and deconvolutions menu ( ).