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.

In the resubmission we processed the calibrated data for Challenges 2
and 4-6, and used the correct precursor ion for Challenge 12.

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:        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.