Using MSGFplus

Data in the package

As LC-MS/MS data tend to be very big, example files are not included in the package. If you do not have access to any LC-MS/MS data but wish to experiment with the package, raw data can be obtained from different data repositories (e.g. PeptideAtlas). The only included data is a very short fasta file (milk-proteins.fasta), that contains the 3 caseins and 2 whey proteins that make up the bulk of proteins in milk.

Building a parameter set iteratively

To create an empty msgfPar object use the eponymous creator function:

par <- msgfPar()
show(par)

## An empty msgfPar object

In general parameters that are not set will use the default value. As a minimum it is needed to specify the fasta file used as a database during peptide search

databaseFile <- system.file('extdata', 'milk-proteins.fasta', package='MSGFplus')
db(par) <- databaseFile

Usually the default parameters aren’t a good match (at least not all of them) and blindly running an analysis with the default values are not recommended. All the different parameters can be accessed and modified using relevant setter and getter methods. In the following they are all illustrated:

tolerance(par) <- '20 ppm'      # Set parent ion tolerance
chargeRange(par) <- c(2, 6)     # Set the range of charge states to look after
lengthRange(par) <- c(6, 25)    # Set the range of peptide length to look after
instrument(par) <- 'QExactive'  # Set the instrument used for acquisition
enzyme(par) <- 'Trypsin'        # Set the enzyme used for digestion
fragmentation(par) <- 0         # Set the fragmentation method
protocol(par) <- 0              # Set the protocol type
isotopeError(par) <- c(0,2)     # Set the isotope error
matches(par) <- 2               # Set the number of matches to report per scan
ntt(par) <- 1                   # Set number of tolerable termini
tda(par) <- TRUE                # Use target decoy approach

par

## An msgfPar object
## 
## Database:                   /tmp/RtmpegGtgN/Rinst1685cd5045bcc7/MSGFplus/extdata/milk-proteins.fasta
## Tolerance:                  20 ppm 
## Isotope error range:        0 - 2 
## TDA:                        TRUE 
## Fragmentation:              0:   As written in the spectrum or CID if no info
## Instrument:                 3:   QExactive
## Enzyme:                     1:   Trypsin
## Protocol:                   0:   No protocol
## No. tolerable termini:      1 
## Peptide length:             6 - 25 
## Charges:                    2 - 6 
## Number of reported matches: 2

As can be seen the show method of the object gives a clear overview over the parameters that have been set.

The last parameter that can be added are modifications to expect in your data. The syntax for this is a bit different as the nature of the parameter is more complex:

mods(par)[[1]] <- msgfParModification(name = 'Carbamidomethyl', 
                                      composition = 'C2H3N1O1', 
                                      residues = 'C', 
                                      type = 'opt', 
                                      position = 'any')
mods(par)[[2]] <- msgfParModification(name = 'Oxidation', 
                                      mass = 15.994915, 
                                      residues = 'M', 
                                      type = 'opt', 
                                      position = 'any')
nMod(par) <- 2                  # Set max number of modifications per peptide

par

## An msgfPar object
## 
## Database:                   /tmp/RtmpegGtgN/Rinst1685cd5045bcc7/MSGFplus/extdata/milk-proteins.fasta
## Tolerance:                  20 ppm 
## Isotope error range:        0 - 2 
## TDA:                        TRUE 
## Fragmentation:              0:   As written in the spectrum or CID if no info
## Instrument:                 3:   QExactive
## Enzyme:                     1:   Trypsin
## Protocol:                   0:   No protocol
## No. tolerable termini:      1 
## Peptide length:             6 - 25 
## Charges:                    2 - 6 
## Number of reported matches: 2 
## 
## Modifications:
## 
## Number of modifications per peptide: 2
## 
## Carbamidomethyl: C2H3N1O1, C, opt, any
## Oxidation:   15.99492, M, opt, any

Other ways to define parameters

Apart from building up the parameters iteratively, they can also be specified at creation time.

par <- msgfPar(database = databaseFile, 
               tolerance = '20 ppm', 
               tda=TRUE,
               instrument='QExactive')

par

## An msgfPar object
## 
## Database:                   /tmp/RtmpegGtgN/Rinst1685cd5045bcc7/MSGFplus/extdata/milk-proteins.fasta
## Tolerance:                  20 ppm 
## TDA:                        TRUE 
## Instrument:                 3:   QExactive

A third method is to read parameter data from a result file generated by MS-GF+. This makes it easy to quickly replicate the parameter used for a certain search in order to compare results.

par <- msgfParFromID('/path/to/results/file.mzid')

par

Running MS-GF+ in batch mode

To start an MS-GF+ run for one or several raw data files the method runMSGF() is used:

res <- runMSGF(par, 'your_rawfile.mzML')

If a vector of file paths are provided, these will be run in succession. By default result files are written besides the original rawfiles with an mzid extension instead of their original extension (silently overriding exiting files). Alternatively a vector of the same length as the number of raw files can be provided to use as save names.

The results are reimported into R as either an mzID or mzIDCollection object, depending on the number of raw files. If importing is not desired it can be avoided with import=FALSE.

Running MS-GF+ asynchronously

If async=TRUE is set in runMSGF() an asynchronous run of MS-GF+ is started. In contrast to running in batch mode, only one file at a time can be run asynchronously and as such it is mostly useful for embedding in code, where you want to use the time when MS-GF+ is running to perform some other tasks in R. When running asynchronously runMSGF() returns an object of class msgfAsync. This object is used to check whether MS-GF+ has finished analysing your code, and in the case that it has finished, import the results.

msgf <- runMSGF(par, 'your_rawfile.mzML', async=TRUE)

while(running(msgf)) {
    Sys.sleep(1)             # You could arguably do more meaningfull stuff here
}
if(finished(msgf)) {
    res <- import(msgf)
}

Running MS-GF+ from a GUI

GUI’s are so nice that it got it’s own package. Check out MSGFgui (also part of Bioconductor) if that is more to your taste. Besides running analysis it got a whole range of ways to investigate your results.