Overview of the GUI
Once the GUI has opened you will be greated by an interface split in two: The left side aids you in selecting data files and setting the parameters for an MS-GF+ analysis. The right side lets you explore the results of the analysis as well as export it to different formats.
Overview of MSGFgui
It should be noted that it is not necessary to use the GUI for running MS-GF+ in order to be able to use the exploratory features of the GUI. Result files generated using MSGFplus or barebone MS-GF+ can be imported, provided the raw data file is still present alongside the result file. This makes it able to reimport older analysis for comparing etc. Do note that the GUI only support results from MS-GF+ and not other peptide identification software, no matter which output format they support. Trying to import other result files will not crash the GUI but be met with an alert during import.
Running MS-GF+ from the GUI
Running MS-GF+
MSGFgui makes it easy for people uncomfortable with command line interfaces to run MS-GF+ analyses. The benfits for other include easy batch-job creation and instant parameter documentation lookup. Everything related to running MS-GF+ is located on the left hand side, and split into file selection and parameter setup.
File selection
In order to run an analysis, two types of files are needed. The obvious one is raw LC-MS/MS data files. MS’GF+ supports most open MS data file formats but encourages the use of mzML. mzML files can be created from proprietary vendor formats using msconvert.
To add a raw data file simple click on the topmost ‘Upload’ button and navigate to the file. Batchjobs are created simply by selection multiple files. In addition a fasta file containing the proteins expected to be in your samples is needed. This is selected by clicking the bottommost ‘Upload’ button. Note that the fasta file should only contain the expected proteins. MS-GF+ creates its own decoy database from this file.
Parameters
Below the file selection area is a list of all parameters that can be set, in order to fine tune the analysis. A full description of all parameters is available in the MS-GF+ documentation but more or less the same information is also available as tooltips when the user hover over the name of the parameter.
There is automatic checking of the parameters, meaning that it should be virtually imposible to supply erroneous parameters to the analysis. The Analyse button is simply not active if the parameters don’t conform, and the violating values will be marked with a red border.
The parameters have a huge influence on the quality of the analysis, so don’t fill them in blindly. Unless you are continuously analysing samples from the same setup, try to experiment with different sensible values until you have reached the optimal setup
Running
Once everything is filled out to your liking it is time to start the analysis. This is done by clicking the aptly named ‘Analyse’ button in the lower right corner of the pane. If the button is not clickable it means you have not supplied the parameters in the right format (see above) so revisit those and fix the errors. Once the analysis is running a progress bar will inform you about how things are proceeding. Results are imported as they are available, but you will experience lag in some operations if you try to interact with the results before the full batch is done.
Investigating the results
Once raw data have been analysed or old results imported it is time to investigate them. MSGFgui tries to distance itself from the usual “provide a table with results and some plots upon double clicking rows”, and tries to implement a more fluid and natural interaction with the identification data.
Navigating the result pane
Toolbar
In the top of the result pane there is a collection of icons providing a toolbar like functionality. From left to right, these are: Import results, Remove results, Export results, Settings and About MSGFgui. Tooltips describing each of them is available upon hovering, but their functionality is pretty self-describing. Exporting can be done in either RDS, xlsx or txt format. Below the toolbar is a tabpane that lets the user select one ofthree tabs. Each of these will be described in the following.
The samples
The first pane, and the one visible at startup, concerns itself with communicating the overall results in a concise way. It contains information such as: number of samples, number of scans, number of identified peptides and proteins etc. as well as overall quality statistics.
The samples pane
The main part of the pane is allocated to two plots. One plot shows the distrubution of score values for peptide-spectrum matches (psm), divided between real (green) and decoy (red) hits. The other plot shows the placement of parent ions from accepted psm’s in a scatter plot. Both of these plots respond to sample selection in the list below them. If multiple samples are selected the plots will shows the union of the selected samples. Beside the sample selection is a list of numeric statistics pertaining to the current selection. The different statistics are rather self-explanatory, but it should be noted that with the exception of psm, all numbers are for the filtered data (see the discussion of the filter pane below). For psm both filtered and unfiltered numbers are given.
The identifications
The second pane is all about investigating the nature of the identification. It is organised in a protein centric way from the belief that most users will primarily be interested in the data in a top-down manner (from protein to peptide to scan).
Proteins
Protein visualisation
Selecting a protein from the leftmost list will result in a visualisation of the protein swooshing in from the left. The visualisation shows the full length of the protein with identified peptides shown above it according to their position on the protein. Peptide identification that pass the current filter are shown in green while those that don’t are shown in grey. In the centre of the visualisation general information about the protein is shown. This information relies upon a properly annotated database file used during analysis.
Peptides
Peptide visualisation
In the centre list all peptides for the selected protein passing the filter is shown. Selecting one will highlight the selected peptide in the visualisation and dim the rest. Furthermore the protein information in the middle of the visualisation is substituted for a representation of the residues making up the peptide sequence. The flanking residues are shown in grey and modification are shown at their corresponding residues if any is present in the peptide.
Scans
Spectrum visualisation
In the rightmost list all scans resulting in a match to the selected peptide and which passes the filter is shown. Clicking on one of them will move the protein arc out of the way to give room for an annotated spectrum plot. Fragment ions will be shown in black with the type on top and the parent ion, if observed, in red. The rest of the ions will be grey. The peptide sequence will still be visible but is now overlayed with the observed fragmentation points, as well as which types of ions support the specific fragmentation. Lastly a small plot in the upper right corner shows the peak that the parent ion is part of along with the position of the parent ion and other sampling points if present.
The Filter
The last tab pane contains a wide variety of options for filtering the identifications. The filtering options are divided horizontally into options related to quality, scan information and peptide information.
Filtering
At the top, the only quality related filtering option is related to setting the FDR cutoff. By default this is set to 0.01 as that is the de facto standard in proteomics. It should be noted that the FDR cutoff is dumb in the sense that it relies on the q-values calculated by MS-GF+. This means that it does not take into account any filtering that is happening in the GUI. Following this is a range of options for trimming down the scans, either by only looking at specific samples, retention times, m/z values or charges. At the bottom is the options for filtering on peptide information. It is possible to choose only to look at peptides related to a subset of proteins or of a certain length, as well as having a specific modification. Once a filter is set it is applied the instance another tab is selected. If the filter is too tight and no psm’s are left it will ask you to loosen the filter. It should be noted that the filtering is provided with the purpose of making it easier to find the information of interest. For instance if one is mostly interested in looking at prtoeins with detected phosphorylation sites, selecting phosphorylation in the modification list will mean that only those proteins where phosphorylated peptides have been identified is visible. On the other hand it is not meant as a way to improve the quality of the results. If this is the interest the MSnID package would be a good starting point. The latter point also means that the filtering is not applied when exporting the results.