ParticipantID:        
Category:	      Category 2
Authors:              Rakesh Kumar [1], Nilesh Kumar [1], Ranjan Nanda [1],
		      Dinesh Gupta [1]
Affiliations:         [1] International Centre for Genetic Engineering
		      and Biotechnology (ICGEB), New Delhi
Automatic pipeline:   Automated
Spectral libraries:   no

Abstract:

1) The molecular formulas from PubChem were stored in local
   library. The MS/MS spectra files (mgf format) were directly used
   for the analysis.

2) At first, all the possible candidate for each principal peak were
   fetched and ranked on the basis of likeness of natural product,
   employing our algorithm and in house script (Python 2.7).  The
   formulas for rest of the sub peaks were also fetched. For each peak
   explained, explanation score was appended for the candidate
   formula.  The molecular formula with highest score was ranked top
   and rest of the formulas ranked accordingly.

3) Finally, INCHI each of the molecular formula was arranged and same
   rank awarded to all INCHI of a particular compound.

ParticipantID:        yuanyuelogsum
Category:             category2
Authors:              Yuanyue Li, Michael Kuhn and Peer Bork
Affiliations:         European Molecular Biology Laboratory, 69117 Heidelberg, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract:

For challenges 1-45, the candidates were retrieved as InChI structures
from PubChem with +/- 6ppm. For challenges 46-243, the molcules from
the category4 (nonredundant) are used as the candidates. We use a new
developed machine learing approach to predict the probability spectrum
for each candidate molecule. Then the score was calculated base on the
similiarity between the probability spectrum and real spectrum. And
the log of the molecules numbers in the model is considered as a
weight. In this approach, all the possible adduct are considered
[M+H]+, [M+Na]+ and [M+NH4]+ for the positive ions, [M-H]-, [M+Cl]-
and [M+COO]- for the negative ions.

ParticipantID:        yuanyuesimple
Category:             category2
Authors:              Yuanyue Li, Michael Kuhn and Peer Bork
Affiliations:         European Molecular Biology Laboratory, 69117 Heidelberg, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract:

For challenges 1-45, the candidates were retrieved as InChI structures
from PubChem with +/- 6ppm. For challenges 46-243, the molcules from
the category4 (nonredundant) are used as the candidates. We use a new
developed machine learing approach to predict the probability spectrum
for each candidate molecule. Then the score was calculated base on the
similiarity between the probability spectrum and real spectrum. In
this approach, only the [M+H]+ is considered for positive ions, the
[M-H]- is considered for negative ions.

ParticipantID:        yuanyuesqrt
Category:             category2
Authors:              Yuanyue Li, Michael Kuhn and Peer Bork
Affiliations:         European Molecular Biology Laboratory, 69117 Heidelberg, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract:

For challenges 1-45, the candidates were retrieved as InChI structures
from PubChem with +/- 6ppm. For challenges 46-243, the molcules from
the category4 (nonredundant) are used as the candidates. We use a new
developed machine learing approach to predict the probability spectrum
for each candidate molecule. Then the score was calculated base on the
similiarity between the probability spectrum and real spectrum. And
the square root of intensity is considered as weight. In this
approach, only the [M+H]+ is considered for positive ions, the [M-H]-
is considered for negative ions.

ParticipantID:        kai_iso
Category:             category2
Authors:              Dührkop, Kai (1) and Ludwig, Marcus (1) and Böcker, Sebastian (1)
		      and Bach, Eric (2) and Brouard, Céline (2) and Rousu, Juho (2)
Affiliations:         (1) Chair of Bioinformatics, Friedrich-Schiller University, Jena
                      (2) Department of Computer Science, Aalto University
                      Developmental Biology, Halle, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract
We processed the peaklists in MGF format using an in-house version of CSI:FingerID. 
Fragmentation trees were computed with Sirius 3.1.5 
using the Q-TOF instrument settings. 

As the spectra were measured in MSe mode we expect to see isotope peaks in
MSMS. We used an experimental feature in SIRIUS that
allows for detecting isotope patterns in MSMS and incorporate them into the fragmentation
tree scoring. 

We used the standard workflow of the SIRIUS+CSI:FingerID (version 3.5)
software: We computed trees for all candidate formulas in the given
structure candidate list from category 4. For challenges 1-45 we
downloaded all candidate structures from our in-house version of
PubChem.

Only the top scoring trees were selected for further processing: Trees
with a score smaller than 75% of the score of the optimal tree were
discarded. Each of these trees was processed with CSI:FingerId as
described in [1]. We predicted for each tree a molecular fingerprint
(with platt probability estimates) and compared them against the
fingerprints of all structure candidates with the same molecular
formula. For comparison of fingerprints, we used the new new maximum
likelihood scoring function which is implemented since SIRIUS 3.5.
The resulting hits were merged together in one list and were sorted by
score. A constant value was added to all scores to make them positive
(as stated in the CASMI rules). Ties of compounds with same score were
ordered randomly. If a compound could not be processed (e.g. because
of multiple charges) its score was set to zero.

[1] Kai Dührkop, Huibin Shen, Marvin Meusel, Juho Rousu and Sebastian
    Böcker Searching molecular structure databases with tandem mass
    spectra using CSI:FingerID.  Proc Natl Acad Sci U S A,
    112(41):12580-12585, 2015.

ParticipantID:        kai112
Category:             category2
Authors:              Dührkop, Kai (1) and Ludwig, Marcus (1) and Böcker, Sebastian (1)
		      and Bach, Eric (2) and Brouard, Céline (2) and Rousu, Juho (2)
Affiliations:         (1) Chair of Bioinformatics, Friedrich-Schiller University, Jena
                      (2) Department of Computer Science, Aalto University
                      Developmental Biology, Halle, Germany
Automatic pipeline:   yes
Spectral libraries:   no

Abstract
We processed the peaklists in MGF format using an in-house version of CSI:FingerID. 
Fragmentation trees were computed with Sirius 3.1.5 
using the Q-TOF instrument settings. 



As the spectra for challenges 46 - 243 were measured in MSe mode we expect to see isotope peaks in
the MSMS. For these challenges we used an experimental feature in SIRIUS that
allows for detecting isotope patterns in MSMS and incorporate them into the fragmentation
tree scoring. 

The preliminary results have shown that we miss a lot of compounds
because we were not always able to identify the correct molecular
formula in top ranks. This might be because no isotope patterns for
the precursor were given. So we prepared a second submission kai112
which is not longer using a hard threshold, but instead consider all
molecular formulas for the CSI:FingerID search and add the SIRIUS
score on top of the CSI:FingerID score.  To avoid that empty trees
(which we would have thrown away by a hard threshold) get high scores
by random, we add a penalty of 1000 if a tree explains not a single
fragment peak. Furthermore, for the kai112 submission we trained
CSI:FingerID on a larger dataset that contains also spectra from NIST.

Beside removing the hard threshold, the kai112 submission follows the
standard SIRIUS+CSI:FingerID protocol: We computed trees for all
candidate formulas in the given structure candidate list from category
4. For challenges 1-45 we downloaded all candidate structures from our
in-house version of PubChem. Each of these trees was processed with
CSI:FingerId as described in [1]. We predicted for each tree a
molecular fingerprint (with platt probability estimates) and compared
them against the fingerprints of all structure candidates with the
same molecular formula. For comparison of fingerprints, we used the
new new maximum likelihood scoring function which is implemented since
SIRIUS 3.5.  Trees with one node get a penalty of 1000. For all other
trees, the SIRIUS score was added to the CSI:FingerID score. The
resulting hits were merged together in one list and were sorted by
score. A constant value was added to all scores to make them positive
(as stated in the CASMI rules). Ties of compounds with same score were
ordered randomly. If a compound could not be processed (e.g. because
of multiple charges) its score was set to zero.

[1] Kai Dührkop, Huibin Shen, Marvin Meusel, Juho Rousu and Sebastian
    Böcker Searching molecular structure databases with tandem mass
    spectra using CSI:FingerID.  Proc Natl Acad Sci U S A,
    112(41):12580-12585, 2015.