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MassBank: a public repository for sharing mass spectral data for life sciences
Author(s) -
Horai Hisayuki,
Arita Masanori,
Kanaya Shigehiko,
Nihei Yoshito,
Ikeda Tasuku,
Suwa Kazuhiro,
Ojima Yuya,
Tanaka Kenichi,
Tanaka Satoshi,
Aoshima Ken,
Oda Yoshiya,
Kakazu Yuji,
Kusano Miyako,
Tohge Takayuki,
Matsuda Fumio,
Sawada Yuji,
Hirai Masami Yokota,
Nakanishi Hiroki,
Ikeda Kazutaka,
Akimoto Naoshige,
Maoka Takashi,
Takahashi Hiroki,
Ara Takeshi,
Sakurai Nozomu,
Suzuki Hideyuki,
Shibata Daisuke,
Neumann Steffen,
Iida Takashi,
Tanaka Ken,
Funatsu Kimito,
Matsuura Fumito,
Soga Tomoyoshi,
Taguchi Ryo,
Saito Kazuki,
Nishioka Takaaki
Publication year - 2010
Publication title -
journal of mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 1076-5174
DOI - 10.1002/jms.1777
Subject(s) - chemistry , mass spectrometry , mass spectrum , electrospray ionization , database , similarity (geometry) , analytical chemistry (journal) , computer science , chromatography , artificial intelligence , image (mathematics)
MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron‐ionization mass spectrometry(EI‐MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)‐MS n data of 2337 authentic compounds of metabolites, 11 545 EI‐MS and 834 other‐MS data of 10 286 volatile natural and synthetic compounds, and 3045 ESI‐MS 2 data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI‐MS 2 data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass‐to‐charge ratio are optimized to the ESI‐MS 2 data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI‐MS 2 data on an identical compound under different collision‐induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21–23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. Copyright © 2010 John Wiley & Sons, Ltd.