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example
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EIC.m
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PAVE workflow documentation.docx
PAVE_main.m
PAVE_main_single.m
PAVE_stat.m
PVAE_output.m
README.md
adduct_list_config.xlsx
atomcount_disp.m
boundary99.mat
db_master.xlsx
inlist.m
pattern.m
pattern_finder.m
pave_atomcount.m
pave_dbsearch.m
pave_find_adduct.m
pave_find_dimer.m
pave_find_dup.m
pave_find_lowC.m
pave_identify_frag.m
pave_junkremover.m

README.md

PAVE (Parent ion Annotation and Verification Engine) is a set of matlab code for untargeted metabolites identification from LC-MS data using 13C and 15N isotopic labelings.

Instructions for using PAVE and running the example (requirement: Matlab 2015a or later):

  1. Download or make a clone of the entire repository. (Note: due to large file size restrictions, the example LC-MS data are not included here, see step 2)
  2. Follow the link below to download the LC-MS data files and save them in the folder "\example" https://drive.google.com/open?id=1C7gwniCSwdk2Povc0KpA_BBLbuCkzMRo There are two large files: "M_neg_yeast.mat" and "M_CID_neg_yeast.mat", which are generated from the original .mzXML files using the code in \tools\parse_neg.m and are required to run the PAVE example. There's also a folder named "raw" which contains all the original .mzXML files for generating the above two files.
  3. Go to the folder "\example" and run PAVE_ini. This will initialize settings and load required data and configurations. (see PAVE workflow documentation for details) There are two files: "Peaklist_yeast_neg.csv" and "CID_neg_yeast.csv", which are generated using Peak detection function in El-maven software package.
  4. Run PAVE_main (Stepwise batch runs for all the peaks). This will take a few hours. After the run is completed, please find "pks" in the workspace, which contains all the annotation results. Can use copy & paste or convert it into a table typing: tb = struct2table(pks) first and then save it as a excel spreadsheet using xlswrite().
  5. Alternatively, for quick testing, select a single peak or a few peaks (e.g., type in the matlab commond line: i=3, or i=[5,7,13], or else) and then Run PAVE_main_single. This will work on the selected peaks only. Please find "pk" in the workspace for the results.
  6. (optional) type PAVE_stat to find out the statistics of features and adducts of all categories.
  7. (optional) use atomcount_disp(M,pk,settings,rep) to visualize and verify the labeling pattern matching for C/N atomcount of a single pk.

PAVE_main goes through the following steps for each peak:

  1. pave_find_dup.m -- remove duplicate peaks within the list
  2. pave_atomcount.m -- Solve Carbon and Nitrogen counts for biological peaks. Those cannot be solved are background peaks
  3. pave_junkremover.m -- annotate isotopes, adducts, multicharged and dimers
  4. pave_identify_frag.m -- annotate fragments subject to CID enhancement
  5. pave_find_lowC.m -- annotate high mass peaks with unsually low Carbon number (possible adducts)
  6. pave_dbsearch.m -- formula match with the database

Please refer to the PAVE workflow documentation for the details of each function. UPDATE: the gui version of pave is available: https://github.com/xxing9703/PAVE2.0

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