Dear all,

I am a newcomer to LC-MS/MS data analysis and I would like to ask a question before starting the data analysis. I would like to quantify the dynamic changes in the whole proteome between two species. I have received the raw files and I have used MSconvertGUI to convert the raw format to mzML format to get started.

Considering the example below, there are two patients and the samples was ran in triplicate.

-> Should I open the files one by one on R? Is there another easier way to load all the data at once?

-> What is the better way to organize the data to compare the samples coming from the patients infected with bacteria 1 and 2?

Bacteria 1

20180202 LA 02 run01.raw; 20180202 LA 02 run02.raw ; 20180202 LA 02 run03.raw ;

20180202 LA 04 run1.raw; 20180202 LA 04 run2.raw; 20180202 LA 04 run3.raw;

Obs.: ."02" = sample that came from a patient; ."04" = sample that came from a patient

Bacteria 2

20180202 LA 03 run01.raw; 20180202 LA 03 run02.raw; 20180202 LA 03 run03.raw;

20180218 05 run01.raw; 20180218 05 run02.raw; 20180218 05 run03.raw

Obs.: ."03" = sample that came from a patient; ."05" = sample that came from a patient

I would be grateful if you could give some tips.

Thanks in advance!

What kind of quantitation are you planning to use? It will be a label-free approach, but depending on whether you want to use spectral counting or M1 label-free, you will need different software. You will also need to search your data against a protein database using a search engine (Mascot, MSGF+, ....). From there on, you can have a look at MSnID for spectral counting. For MS1 label-free, you are better off looking at something like Proteome Discoverer (which you might have access to given that you have Thermo data), or MaxQuant (that will also search the peptides using its own search engine).

You can load all the raw data at once in R - see MSnbase::readMSData(..., mode = "onDisk") for details.

Whatever quantitation method you use, there will always be a way to get back to R for the analysis of your quantitation data.