## Warning in fun(libname, pkgname): Java need to be at least version 1.7 for
## MS-GF+ to work. Please upgrade.

This document is intended to guide the user through the different aspects of using MSGFgui to perform peptide identification from raw LC-MS/MS data and inveestigate the results. Note that this document will not describe the inner workings of MS-GF+ - the algorithm that performs the identification. For users interested in the nitty-gritty of the algorithm please see the MS-GF+ webpage.

This package comes with a sister package, MSGFplus, that provides the interface to the original MS-GF+ java code. If you wish to implement MS-GF+ analysis within your own functions or packages, MSGFplus provide a command-line interface to MS-GF+. This package, conversely, only provides a GUI overlay and a symphony of visualisations coded in Javascript (using D3.js) that cannot be accessed through R code.

MSGFgui is being maintained at its GitHub repository, where bug reports and feature requests are happily accepted.

System requirements

Well obviously you need R, but lets assume you got that covered. The main point is that working with proteomics data puts some strain on your system. I don’t want to throw you a list of specs you should compare your system to, but do know that the kind of analysis facilitated by the GUI is best suited for a workstation class system. That means lots of memory and a multitude of cores. When that is said there is nothing about the GUI itself that requires a mighty machine, so if you have some small (~200 mb) raw files you’ll have no problem playing around with the GUI on a decent laptop.

WARNING: Due to some unfortunate incompatibility between shiny, mzR/Rcpp and RStudio, running MSGFgui through RStudio will cause the R session to crash once raw data is trying to get accessed (retrieving raw MS/MS scans). This is not a problem when running R in the standard way. This problem will hopefully soon be adressed and this warning will disappear.

Opening the GUI

As this package is all about a graphic user interface, it exposes very few functions to the user (only 2). The one that you will probably use most often is MSGFgui() (the other one will be discussed in a bit). In all its simplicity the GUI is started from the R terminal as such:

# Standard fashion
MSGFgui()

# You can pass parameters along to shiny's runApp()
MSGFgui(port='0.0.0.0')

This will open up MSGFgui in your standard browser. Once the GUI is running the R process is interupted. To regain control of R (and shut down MSGFgui) press the ‘esc’ key.

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.

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