ParticipantID: Dunn and Birmingham
Category: Category1 and category 2
Authors:Members of Dunn and Viant groups at University of Birmingham, UK
Affiliations:University of Birmingham, UK
Automatic pipeline:No
Spectral libraries:No

Abstract

The group automatically applied workflow 2 of PUTMEDID-LCMS to
calculate one or multiple molecular formula that matched the accurate
mass of the neutral metabolite. The group then automatically or
manually searched MMD, KEGG and ChemSpider in this order to define
potential metabolite structures, which were manually filtered in
relation to isotopes present or absent and 12C/13C ratios for
instruments where an accurate ratio can be calculated. The structures
were applied in MetFrag to calculate matches between in-silico
fragmentation and experimental data; these data were manually assessed
to remove biologically unreasonable metabolites.

We processed only the LC-MS challenges as follows:
1,2,3,4,5,6,10,13,14,15,17. The challenge data were converted to
molecular formula(s), searched against MMD, KEGG and Chemspider and
comparison of experimental and in-silico fragmentation data were
compared in MetFrag v0.9.

ParticipantID:        hshen
Category:	      category 1 and 2
Authors:              Huibin, Shen(1) and Nicola, Zamboni(2) and Markus, 
                      Heinonen(3) and Juho, Rousu(1)
Affiliations:         (1) Helsinki Institute for Information Technology; 
                      Department of Information and Computer Science, 
                      Aalto University, Finland (2) Institute of 
                      Molecular Systems Biology, ETH Zurich, Switzerland. 
                      (3) IBISC, Université d’Evry-Val d’Essonne, France
Automatic pipeline:   yes
Spectral libraries:   yes (MassBank)

Abstract

We processed only the LC-MS challenges. We predict the molecular
fingerprints of the challenge data using FingerID and use them to
search the Kegg compound database.

ParticipantID:        kduehrkop
Category:	      category1
Authors:              Kai Dührkop, Kerstin Scheubert, Sebastian Böcker
Affiliations:         Chair for Bioinformatics, Friedrich-Schiller-University, 
                      Jena, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract

We processed only the LC-MS challenges. The spectral data was analyzed
by the sirius command line tool.  We choosed the parameters of our
tool according to the challenge description.  We combined the scores
of the isotopic patterns and the fragmentation trees computed from
ms/ms spectra.  The pubchem molecular formula search was used to
assign adducts to the ions.  If no formula was found in pubchem, we
assumed a protonation.  For intrinsic charged formulas, we added a
protonated variant with a slightly reduced score.

ParticipantID:        rdisop
Category:             category1
Authors:              Neumann, Steffen
Affiliations:         Leibniz Institute for Plant Biochemistry, Dept. of Stress-
                      and Developmental Biology, Halle, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract:

I create a small script extracting the isotope pattern 
from the MS peaklist files, and processed them with the 
Bioconductor package rdisop (which in turn uses the disop library 
developed by the Boecker group). No efforts were made to 
cleverly detect the [M+H]+ or adducts. 

ParticipantID:        schymane
Category:             category1
Authors:              Schymanski, Emma(1) and Meringer, Markus (2)
Affiliations:         (1) Eawag: Swiss Federal Institute of Aquatic Science and 
                      Technology, Überlandstrasse 133, CH-8600 Dübendorf, 
                      Switzerland (2) DLR: German Aerospace Centre,
                      Münchnerstrasse 20, D-82234 Oberpfaffenhofen-Wessling,
                      Germany

Automatic pipeline:   yes
Spectral libraries:   no

Abstract:

We processed Category 1 challenges with MOLGEN-MS/MS. Where no
evidence of halogens was present, we chose the elements
CHNOPS. Default parameters were MS accuracy 5 ppm, MS/MS accuracy 10
ppm, using the existence filter and allowing "OEI" ions to explain
MS/MS peaks. The option to use positive or negative ionisation was
taken from the information on the web, using +H for positive and -H
for negative, except for the tricky cases. We submitted the formulas
generated with these settings, using the combined match value as the
score.

Where multiple MS/MS files were available we combined them to get the
maximum number of peaks, using the peak of highest intensity where the
same peak occurred more than once. As the score was not weighted by
intensity, this should make no difference to the outcome.  Deviations
from these default settings were: Challenge 11: additional setting
m=232.088 to set the M+ ion mass and ion mode "-e" corresponding to an
M+ ion.  Challenge 16: additional setting m=359.1481 to set the M+ ion
mass and ion mode "-e" corresponding to the M+ ion. Could also have
added a H+ to the mass and run mode +H. MSMS match values taken as
score, as MS values were 0.