Hi, Kevin! Hi, Talita do Nascimento! :)

I've worked with Progenesis QI for metabolomics and I find it a good (and expensive) software. I think it is a good strategy to study other (and open source, free) softwares, such as MZmine 2 (http://mzmine.github.io), so you'll have more flexibility when it comes to pre-processing and alignment. I feel I can understand more of my data by using open source softwares, with less automated steps. For ID, I'd recommend GNPS (https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp) and online in silico molecular fragmentation platforms, such as METFRAG (https://msbi.ipb-halle.de/MetFragBeta/) and CFM-ID (http://cfmid.wishartlab.com/).

Hope this will help you.

Kind regards,

Joana.

Hello Kevin,

No complaints against Progenesis QI at all. As long as you got funding and resources to buy the expensive license for annual or perpetual use. To me, it is hugely expensive and not affordable at all. Had many trials and does a decent job- but like any other commercial tool would have so many limitations in data pre-processing and annotation.

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.

Use MassTRIX first: http://masstrix3.helmholtz-muenchen.de/masstrix3/ .

For extensive ID annotations, use MZMine (http://mzmine.github.io/) which is free and can integrate HMDB, PubChem, Metlin, ChemSpider, BioCyc, KEGG) into the software and do data processing and annotations simultaneously, XCMS (locally or XCMSOnline (https://xcmsonline.scripps.edu/landing_page.php?pgcontent=mainPage) which are also free and can be useful in searching the same DBs as above, meta-P does fine (free), or you can do one-by-one at mzCloud (free, https://www.mzcloud.org/), at METLIN DB (also free: https://metlin.scripps.edu/). Use Mass++ (v, also free) to analyze the spectra and do simultaneous searches for the spectra at MassBank http://www.massbank.jp/?lang=en, free) too. You can also search against MoNA:http://mona.fiehnlab.ucdavis.edu/spectra/browse, also free!

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.

You have had impressive work and publications in analytical chemistry research, and you are welcome to collaborate and I can learn some more from you and extend some of my expertise in metabolomics as well! :  )

Thanks and regards,

Biswa

Thanks for your thorough answer, Biswa! 

Yes I advise you to use  http://workflow4metabolomics.org/. Uploading is easy and fast, and all the processing steps are well interfaced (clickable) and explained, and run fast. 

J. P. S. Oliveira

Hi, Kevin! Hi, Talita do Nascimento! :)

I've worked with Progenesis QI for metabolomics and I find it a good (and expensive) software. I think it is a good strategy to study other (and open source, free) softwares, such as MZmine 2 (http://mzmine.github.io), so you'll have more flexibility when it comes to pre-processing and alignment. I feel I can understand more of my data by using open source softwares, with less automated steps. For ID, I'd recommend GNPS (https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp) and online in silico molecular fragmentation platforms, such as METFRAG (https://msbi.ipb-halle.de/MetFragBeta/) and CFM-ID (http://cfmid.wishartlab.com/).

Hope this will help you.

Kind regards,

Joana.