CASMI: And the Winner is...

被引:28
作者
Schymanski, Emma L. [1 ]
Neumann, Steffen [2 ]
机构
[1] Eawag Swiss Fed Inst Aquat Sci & Technol, Uberlandstr 133, CH-8600 Dubendorf, Switzerland
[2] IPB Leibniz Inst Plant Biochem, Dept Stress & Dev Biol, DE-06120 Halle, Saale, Germany
关键词
mass spectrometry; metabolite identification; small molecule identification; contest; metabolomics; non-target identification; unknown identification;
D O I
10.3390/metabo3020412
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 [生物化学与分子生物学]; 081704 [应用化学];
摘要
The Critical Assessment of Small Molecule Identification (CASMI) Contest was founded in 2012 to provide scientists with a common open dataset to evaluate their identification methods. In this review, we summarize the submissions, evaluate procedures and discuss the results. We received five submissions (three external, two internal) for LC-MS Category 1 (best molecular formula) and six submissions (three external, three internal) for LC-MS Category 2 (best molecular structure). No external submissions were received for the GC-MS Categories 3 and 4. The team of Dunn et al. from Birmingham had the most answers in the 1st place for Category 1, while Category 2 was won by H. Oberacher. Despite the low number of participants, the external and internal submissions cover a broad range of identification strategies, including expert knowledge, database searching, automated methods and structure generation. The results of Category 1 show that complementing automated strategies with (manual) expert knowledge was the most successful approach, while no automated method could compete with the power of spectral searching for Category 2-if the challenge was present in a spectral library. Every participant topped at least one challenge, showing that different approaches are still necessary for interpretation diversity.
引用
收藏
页码:412 / 439
页数:28
相关论文
共 48 条
[1]
CASMI-The Small Molecule Identification Process from a Birmingham Perspective [J].
Allwood, J. William ;
Weber, Ralf J. M. ;
Zhou, Jiarui ;
He, Shan ;
Viant, Mark R. ;
Dunn, Warwick B. .
METABOLITES, 2013, 3 (02) :397-411
[2]
[Anonymous], 2012, IUPAC INT CHEM ID
[3]
MOLecular structure GENeration with MOLGEN, new features and future developments [J].
Benecke, C ;
Gruner, T ;
Kerber, A ;
Laue, R ;
Wieland, T .
FRESENIUS JOURNAL OF ANALYTICAL CHEMISTRY, 1997, 359 (01) :23-32
[4]
Bocker S, 2006, LECT NOTES COMPUT SC, V4175, P12
[5]
SIRIUS: decomposing isotope patterns for metabolite identification [J].
Boecker, Sebastian ;
Letzel, Matthias C. ;
Liptak, Zsuzsanna ;
Pervukhin, Anton .
BIOINFORMATICS, 2009, 25 (02) :218-224
[6]
Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets [J].
Brown, Marie ;
Wedge, David C. ;
Goodacre, Royston ;
Kell, Douglas B. ;
Baker, Philip N. ;
Kenny, Louise C. ;
Mamas, Mamas A. ;
Neyses, Ludwig ;
Dunn, Warwick B. .
BIOINFORMATICS, 2011, 27 (08) :1108-1112
[7]
Unsupervised data base clustering based on Daylight's fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets [J].
Butina, D .
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1999, 39 (04) :747-750
[8]
Daylight, 2012, SMILES A SIMPL CHEM
[9]
Duhrkop K., 2013, METABOLITES UNPUB
[10]
MetFusion: integration of compound identification strategies [J].
Gerlich, Michael ;
Neumann, Steffen .
JOURNAL OF MASS SPECTROMETRY, 2013, 48 (03) :291-298