Tool Buttons - Precursor Map
The precursor map shows the unique precursors in your run. Each dot on the map represents a unique precursor, where uniquness is defined by mass, charge and retention-time.
Each unique precursor may represent several individual scans,especially if you search batches of datafiles.
Each precursor can be selected and the scans can be viewed.
The precursor map can be used both prior to a search and after for different purposes.
Post-search precursor map of a protein cross-linked with BS3.
Crosslinks are highlighted in blue.
Here we have swiched to 3D mode, where the Y-axis shows the precursor intensities.
Once you have selected the mgf files to be included into your search in the main window, press the "LOAD FILES
NOW" button. This loads the datafiles into memory, and automatically displays the precursor map.
The precursor map is divided into three columns:
The left hand column lists the precursors in your dataset according to its mass, charge, retention time, observations
and other useful information.
The central column displays the graphical precursor map. Each precursor is represented by a coloured dot, where the
colour can represent various features. These are selected from the checkboxes in the rightmost column.
Once you have loaded a dataset, the precursormap will typically look like the one below.
Use the checkboxes on the right to highlight different features in your dataset
Precursors coloured according to charge
Precursors with "golden pair" peaks highlighted in orange
Precursors with "golden pairs" highlighted. Everything else de-selected
Another sample of glyco-proteins.
Identified glyco-peptides are highlighted in light-blue.
Notice in the last picture, how we have de-selected all precursors except those with recognised
"golden pairs". Any annotated group can be visible or hidden like this.
You can then choose to save all scans relating to the visible precursors into a new mgf file. This
way you can split your dataset into several discrete groups for later use.
Here is the same dataset as above, after a search has
concluded. It is a sample of a glycoprotein, which has
been enriched for glycopeptides through a Zic-Hilic
Notice how the precursor points have changed colour.
Light blue represents identified glycopeptides.
Dark blues are crosslinked peptides (in this case
Greens are standard peptides
Yellows represents results below the minimum score
see the below figure for further details.
Use of the precursor map in datamining
You can use the precursormap in combination with the datamining features on the main window.
Here we have a sample which is heavily contaminated with PEG. This of course reflects on the datafile and leads to
longer search times.
We then use the "Annotate Fragment Groups" feature to highlight scans, where PEG has been detected (grey dots).
Left click and hold your mousebutton, while dragging a window om
the precursor map to zoom in on a target cluster of precursors. Then
click the "show selected group" button to bring up the scans.
Select or browse through the
contaminated scans to see the
We notice the tell-tale signs of
repetitive 22/44 Da patterns from the
Select "fragment annotation" on the precursor map and de-select
everything else, so that only the PEG related precursors are visible, while
all other precursors are hidden from the map.
Then click "Delete all visible precursors" to remove only the involved
scans from the dataset. The precursor map will now be completely blank.
Tick the checkboxes in order to make the hidden precursors visible
again. If you do this prior to a search, this would be the entry named "No
Finally, press the "Save visible to MGF" button, to save the remaining
scans into a new MGF file - No changes are ever made to your original
Using this approach, entire batches of peak-lists can be cleaned and
concentrated into a single new MGF file. Such noise-reduced data files,
can then be used for future searches.
By hiding, showing and saving the various groups of precursors, you can
split your dataset into separate files containing individual groups of
Use of the Precursor map in quality control
Knowing the charge of your precursors can be very helpful for a number of purposes:
When you work with charge dependant methods, such as SCX, you can use the precursor map to
follow the overall charge increase of your various fractions.
Working with tryptic digests of cross-linked peptides, most of the cross-links are found in the triply
Generally you don't want too many singly charged scans. etc..
Here, you can toggle each charge state on or off - prior to running the actual search.
Precursor map with no annotations, and the same with charge states highlighted
The fragment filter annotates the average number of
peaks for each precursor.
This allows you to view and keep the most promising
scans - again prior to the actual search.
Here we observe that our dataset consists primarily of well
fragmented precursors (green), with a scattering of singly
charged, poorly fragmenting scans (red).
Applying both filters simultaneously, we decide to reject the
singly charged scans, and also scans with less than 50 peaks.
We can then export the corresponding scans to a new MGF
file for future use, or for use in other search engines.