ParticipantID:        BertrandSamuel 
Category:             category1 
Authors: 	      Bertrand, Samuel(1) and Guitton, Yann (1,2,3,4,5) 
Affiliations:         (1) Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences 
                      Pharmaceutiques et Biologiques, Université de Nantes, France 
                      (2) Université de Rennes 1, IRISA UMR 6074, Rennes, France 
                      (3) CNRS, IRISA UMR 6074, Rennes, France 
                      (4) Centre Rennes-Bretagne-Atlantique, Projet Dyliss, INRIA, 
                      Rennes Cedex, France 
                      (5) Université de Nantes, LINA UMR 6241, Nantes, France 
Automatic methods:    yes 

Abstract 
The challenge data were automatically treated using R, Rdisop, RMassBank as follow: 
1- retrieving MS Data 
2- searching for NeutralLosses, adducts according to MS1 data. When no information 
were found ions were considered as [M]+, [M+H]+, [M+Na]+, [M+K]+, [M+H+solvent]+, 
[M+NH4]+ (if ammonia was added in the eluant), [M-H]-, [M+Fa]- (if Fa was added) and 
[M+AceticAcid]- (if AA was added); some manual interventions were done to 
simplify some complex detected ion relationship 
3- for each adduct possibility of each challenge molecular formula were deduced by 
Rdisop using CxHyO27N25P9F34Fe (modified according to neutral losses and adduct 
assigned) as maximum allowed atoms in the very strict limit of the MS accuracy. Each
possible formula should pass the seven golden rules to be saved.
4- each possible formula were scored according to MS accuracy, Spectral Accuracy, MF 
redundancy (multiply detected adducts from one MS spectra), type of adduct, the 
possibility to find Molecular formula related to MS2 ions from the MF itself 
5- only formula found in chemSpider/chebi/KnApSaCK were reported for challenges 1-42 
(no filtering according to existence were done for challenges 43-48)

ParticipantID:        lridder
Category:             category2
Authors:              Ridder, Lars
Affiliations:         Wageningen University, Laboratory of Biochemistry,
                      Wageningen, The Netherlands
Automatic methods:    yes

Abstract
The challenge peak lists were converted to MAGMa input files, and processed
with MAGMa (http://www.emetabolomics.org/magma), as described in the
metadata file for category 2.
Submissions for category 1 consists of the lists of unique molecular formula's
obtained in category 2. The provided scores correspond to the highest scoring
candidate (in category 2) with the given molecular formula.
This submission is supported by the observation in Ridder et al. (2012) and Ridder
et al. (2013) that, even if the top scoring candidate structure in MAGMa is
not correct, the molecular formula often is.

References:
Ridder, L.; van der Hooft, J. J. J.; Verhoeven, S.; de Vos, R. C. H.; van Schaik, R.;
Vervoort (2012) J. Rapid Commun. Mass Spectrom. 26, 2461-2471.
Ridder, L.; van der Hooft, J. J. J.; Verhoeven, S.; de Vos, R. C. H.; Bino, R. J.;
Vervoort, J.(2013) Anal. Chem. 85, 6033-6040.

ParticipantID:        tpluskal
Category:             category1
Authors:              Pluskal, Tomáš(1)
Affiliations:         (1) Okinawa Institute of Science and Technology
                      (OIST), Onna, Okinawa, Japan
Automatic methods:    no

Abstract
I imported the challenge spectra into MZmine version 2.13.1 through a small 
custom-made import module. Then I generated chemical formulas using the MZmine’s 
chemical formula prediction module. I did not alter the resulting lists of chemical 
formulas generated by MZmine. However, I had to manually adjust the parameters for 
individual challenges, therefore I do not claim this method is fully automatic.

MZmine requires 7 parameters to be set for prediction of chemical formulas:

1) Neutral mass
To calculate the neutral mass from the monoisotopic m/z, I set the ion type to [M+H]+
for positive mode spectra or [M-H]- for negative mode spectra. The only exceptions 
were challenges #8, #23 and #26, which I believe represented [M+Na]+ ions, based on 
manual observation of the spectra, or empirical knowledge. Charge was assumed to be 1
in all cases.

2) m/z tolerance was set to twice the expected m/z tolerance for the instrument, as 
stated in the challenge description (if the challenge said "The expected mass 
accuracy of the instrument is <5ppm", I set the m/z tolerance to 10 ppm).

3) Element counts
For challenges #1 to #31, I allowed C,H,N,O,P,S elements (0-1000 each). For 
challenges #32 to #48 (synthetic and unknown compounds) I also added elements F,Cl,
Br,I (0-100 each).

4) Element count heuristics
All restrictions available in MZmine were activated (H/C ratio, NOPS/C ratios, 
multiple element counts). These are originally based on the Seven Golden Rules paper 
by Kind and Fiehn, 2007.

5) RDBE restrictions
Generated formulas were restricted to those having integer RDBE values in the range 
of 0-40.

6) Isotope pattern filter
Isotope pattern m/z tolerance was fixed to 0.02 m/z in all cases, because input data 
did not contain any fine isotopic peaks. Minimum absolute intensity for generated 
patterns was set to 0.1. Minimum isotope pattern similarity score was set to 90% 
except challenges #24 (50%, due to declared bias in isotope intensity), #27 (80%, due 
to no candidates returned at 90%) and #35 (80%, due to no isotopes observed for 
209.0453 m/z).

7) MS/MS filter
MS/MS m/z tolerance was set to 0.005 m/z in all cases. MZmine uses this tolerance to 
interpret neutral losses (e.g., 18.0106 = H2O). Minimum MS/MS score was set to 90%, 
except challenges #6, #8, and #18, which contained MS/MS peaks that I was not able to 
interpret (even manually). For these three challenges I disabled the MS/MS filter 
completely.

ParticipantID:        felicityallen
Category:             Category 1 and Category 2
Authors:              Felicity Allen, Russ Greiner, David Wishart
Affiliations:         Department of Computing Science University of Alberta, Canada
Automatic methods:    yes

Abstract
A list of candidate molecular formulae was obtained by querying PubChem
for all structures within the stated MS tolerances of the precursor 
mass (as obtained by combining the integer precursor mass stated in the
experimental details file with the MS1 file). In cases where multiple
precursor masses were listed, candidates and molecular formula were included
for all listed masses. All unique molecular formulae were collated from these 
structures.

The MS1 spectra were then predicted for each candidate molecular formula using
the emass program by A. Rockwood and P. Haimi [1].  These predicted spectra
were compared to the provided MS1 spectra (restricted to within 10 Da of the 
monoisotopic mass of the molecular formula), and an MS1_SCORE was produced 
for each molecular formula based on the closeness of this match. The scoring
metric used was:  
MS1_SCORE = ( (WP + WR + DP)_3ppm + (WP + WR + DP)_5ppm + (WP + WR + DP)_10ppm )/10
where 
WP = intensity weighted precision (0-100)
WR = intensity weighted recall (0-100)
DP = dot product (0-1) x 100

[1] Rockwood A. and Haimi P., "Efficient calculation of accurate masses of isotopic 
peaks.", Journal of the American Society for Mass Spectrometry, 17:3 p415-9 2006.	

A list of candidate structures was obtained by querying all of the following
databases for all candidates within the required mass ranges (determined as above):
HMDB  http://www.hmdb.ca/
ChEBI http://www.ebi.ac.uk/chebi/
ChEMBL https://www.ebi.ac.uk/chembl/
Metlin http://metlin.scripps.edu/
FOODB http://foodb.ca/
T3DB http://www.t3db.ca/
DrugBank http://www.drugbank.ca/
ECMDB http://www.ecmdb.ca/
YMDB http://www.ymdb.ca/
PubChem https://pubchem.ncbi.nlm.nih.gov
PlantDB Privately held list of 200,000 plant and plant-derived compounds.

For all candidate structures with an MS1_SCORE > 28, CFM-ID [2,3] was used
to predict an MS2 spectrum for all candidates at both a 10V and 20V energy level. 
The MS2_SCORE was computed for each to reflect the closeness of this match:

MS2_SCORE = (DP + 2JA + WP + WR)_10V + (DP + 2JA + WP + WR)_20V
where WP, WR and DP are as above, and
JA = Jaccard Score (0-1) x 100

[2] Allen F., Pon A., Wilson M., Greiner R., Wishart D., "CFM-ID: A web server for 
annotation, spectrum prediction and metabolite identification from tandem mass 
spectra", Nucleic Acids Research, Web Server Edition 2014.

[3] Allen F., Greiner R., Wishart D., "Competitive Fragmentatation Modeling of 
ESI-MS/MS spectra for putative metabolite identification", Metabolomics, 11:1, 
p98-110, 2015.

For all candidates, a DB_SCORE was produced according to which of the above databases 
it was found in, to make use of the provided information about metabolic origin. 
Consider the database names below as indicators, i.e. add the amount if in that 
database):

DB_SCORE (Challenge 1-15) = HMDB (+200) + METLIN (+100) + ChEBI (+20) + FooDB (+20) +
DrugBank (+20) + ECMDB (+10) + YMDB (+10) + ChEMBL (+10).
DB_SCORE (Challenge 6-31) = ChEBI (+100) + ECMDB (+100) + YMDB (+100) + PlantDB 
(+100) + Metlin (+50) + FooDB (+50) + ChEMBL (+10).
DB_SCORE (Challenge 32-42) = HMDB (+50) + ChEMBL (+100) + T3DB (+100) + METLIN (+100)
+ DrugBank(+100)
DB_SCORE (Challenge 43-48) = 0

An additional manual offset of +/-50 was also made in some cases to reorder the top 
couple of candidates. This was done where descriptions of compounds in the database 
provided additional information for or against a match e.g. where a particular 
species or biofluid was provided in the experimental data.

For Category 2, the results were ranked according to the sum of the above three 
scores: TOTAL_SCORE = MS2_SCORE + DB_SCORE + MS1_SCORE

For Category 1, the molecular formula was computed for each structure
from Category 2 and kept in the same order. The list was then processed
to remove duplicate entries, keeping only the highest ranked listing 
for each unique molecular formula.

ParticipantID:        avaniya
Category:	          Category 1
Authors:              Vaniya, Arpana, Yan Ma, Dominique Ardura, Zijuan Lai, 
                      Mine Palazoglu, Sajjan Singh Mehta, Gert Wohlgemuth, 
                      Tobias Kind, Oliver Fiehn.
Affiliations:	      Genome Center, University of California, Davis 
Automatic methods:    Yes

Abstract:	
The challenge peak lists were converted to MSP files, and processed with Seven Golden
Rules and Formula Finder. The results were extracted from the Excel sheet. The 
isotope pattern of each potential formula was simulated and compared to the MS1 
spectrum of each challenge. The similarty was compared using Mass Frontier 7.0 where 
only the highest scoring pattern was be selected as the the best molecular formula.   
Databases and MS/MS mass spectral library search was performed to further verify the 
molecular formula. The Human Metabolome Database (HMDB) and MS/MS library search 
using NIST 14, MassBank, METLIN, ReSpect were used in this category. The scores from 
Seven Golden Rules were used for submission to CASMI.

ParticipantID:        Fitch 
Category:             Category 1 and Category 2
Authors:              William Fitch (1)
Affiliations:         (1) Stanford University (bfitch@stanford.edu)
Automatic methods:    No 

Abstract
I used manual methods to generate all structures and formulas. I did not have a good 
formula generator that used isotopic ratios. I did use ChemCalc sometimes and 
Xcalibur’s spectrum simulator. MS/MS interpretation was purely manual with a few 
database searches for confirmation. The dereplication choices among isomers were 
highly subjective based on experience and likely abundance in stated samples.

MS data is not often adequate to select among isomers.  We need LC retention. You 
gave me that data but I didn’t know how to use it. I recently published a 
metabolomics paper (Rapid Comm Mass Spec 2013, 27, 2091) in which I hinted at methods 
for making interlab retention data reproducible using sets of internal standards and 
well identified components. Unfortunately I have not found funding to pursue this 
avenue further.

Two other ideas for assisting these structure determinations, especially the unknowns 
would be deuterium exchange and retention at 2 ph values. Determining the number of 
exchangeable hydrogens is simple to perform, can help select among dereplication 
isomers and gives extra clues with true unknowns. The retention at 2 pH experiment 
requires some standardization but has the potential to indicate acid base character 
of the unknown.