Hi,

usually  the software that is provided from one of the MS instrument producers are not free, but you will probably get a good deal on them when you purchase the instrument. These are then capable of reading the instrument specific files, which of course are specific for each company, therefor you would probably need to convert your raw data file to a different format, such as mzXML (you can understand this with the comparison of changing a MS Word file to a txt file). This can be done by several different programs, the one I use can be downloaded from:

http://proteowizard.sourceforge.net/

You did not really explain a lot about what you are doing exactly, but the work I did for metabolites, could be nicely analyzed by XCMC which is free. You can access it on:

https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage

there are also a few papers published about it in Analytical Chemistry which describe the features to make the start a bit easier for you. 

good luck

Hi,

usually  the software that is provided from one of the MS instrument producers are not free, but you will probably get a good deal on them when you purchase the instrument. These are then capable of reading the instrument specific files, which of course are specific for each company, therefor you would probably need to convert your raw data file to a different format, such as mzXML (you can understand this with the comparison of changing a MS Word file to a txt file). This can be done by several different programs, the one I use can be downloaded from:

http://proteowizard.sourceforge.net/

You did not really explain a lot about what you are doing exactly, but the work I did for metabolites, could be nicely analyzed by XCMC which is free. You can access it on:

https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage

there are also a few papers published about it in Analytical Chemistry which describe the features to make the start a bit easier for you. 

good luck

You could also try MZmine. It should be able to open the .raw files from Thermo directly.

Best, F

Mavin out of Joshua D. Rabinowitz's lab is very powerful and user friendly.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4055029/

Take a look on gnps, for comprehensive spectral library and molecular networking:

http://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp

Hello,

Majority of the freely available tools, software, and databases from 2014-2015 for your analyses have also been recently summarized/ reviewed by us at Updates in metabolomics tools and resources: 2014–2015 (http://onlinelibrary.wiley.com/doi/10.1002/elps.201500417/abstract), please check out the Table-1 and find your tools!!! : )

Use msConvert from Proteowizard to convert the .RAW files from Thermo to .mzXML formats to proceed in first place.

The extracted raw values need to be normalized, scaled, log transformed (or other methods) and so on to see the real effects than counts!

QC wise, (i) you can check for batch variations for example using 'limma' package on R or say normalyzeR or LOESS-N normalization and so on; (ii) internal standard/ QC-pooled runs or even blank runs, based normalization would also tell you the distribution of data/ values, (iii) TIC values and distribution and so on- both pre-processing and statistical tools would let you know the quality of the runs.

I would suggest to run the data through simple WebServer as MetaboAnalyst http://www.metaboanalyst.ca/ or say, WorkflowforMetabolomics: http://workflow4metabolomics.org/ or say XCMSOnline https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage to see the data quality, trends, basic stats, and so on before starting with R and playing with options one by one. Of course bioconductor/R and Python or even Matlab or Galaxy would lead you into a big and different world of analysis of metabolomics data sets.

In our review you can go to the 'holistic tools' section to find many of these press-button software suites to get started and reduce data - in terms of removing "obvious" outliers, multivariate analysis tools, to find significant changes, IDs which do not make sense (MB Role ID conversion, or say CTS@ Fiehn lab) etc. would help a lot.

Caution: With direct injection (i.e., no separation) data interpretation would need a lot manual intervention and would be a laborious process. I wonder why would not you use LC- separation!

Best wishes.

Hope it helps.

Thanks,

Biswa

What do you mean by "interpret" once you get MASCOT output?