Literature DB >> 20623627

MassBank: a public repository for sharing mass spectral data for life sciences.

Hisayuki Horai1, Masanori Arita, Shigehiko Kanaya, Yoshito Nihei, Tasuku Ikeda, Kazuhiro Suwa, Yuya Ojima, Kenichi Tanaka, Satoshi Tanaka, Ken Aoshima, Yoshiya Oda, Yuji Kakazu, Miyako Kusano, Takayuki Tohge, Fumio Matsuda, Yuji Sawada, Masami Yokota Hirai, Hiroki Nakanishi, Kazutaka Ikeda, Naoshige Akimoto, Takashi Maoka, Hiroki Takahashi, Takeshi Ara, Nozomu Sakurai, Hideyuki Suzuki, Daisuke Shibata, Steffen Neumann, Takashi Iida, Ken Tanaka, Kimito Funatsu, Fumito Matsuura, Tomoyoshi Soga, Ryo Taguchi, Kazuki Saito, Takaaki Nishioka.   

Abstract

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. 2010 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 20623627     DOI: 10.1002/jms.1777

Source DB:  PubMed          Journal:  J Mass Spectrom        ISSN: 1076-5174            Impact factor:   1.982


  514 in total

1.  Correlation analysis of proteins responsive to Zn, Mn, or Fe deficiency in Arabidopsis roots based on iTRAQ analysis.

Authors:  Sajad Majeed Zargar; Masayuki Fujiwara; Shoko Inaba; Mami Kobayashi; Rie Kurata; Yoshiyuki Ogata; Yoichiro Fukao
Journal:  Plant Cell Rep       Date:  2014-11-01       Impact factor: 4.570

2.  Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.

Authors:  Ludovic C Gillet; Pedro Navarro; Stephen Tate; Hannes Röst; Nathalie Selevsek; Lukas Reiter; Ron Bonner; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2012-01-18       Impact factor: 5.911

3.  Revisiting the reactivity of uracil during collision induced dissociation: tautomerism and charge-directed processes.

Authors:  Daniel G Beach; Wojciech Gabryelski
Journal:  J Am Soc Mass Spectrom       Date:  2012-02-14       Impact factor: 3.109

4.  A method of finding optimal weight factors for compound identification in gas chromatography-mass spectrometry.

Authors:  Seongho Kim; Imhoi Koo; Xiaoli Wei; Xiang Zhang
Journal:  Bioinformatics       Date:  2012-02-13       Impact factor: 6.937

5.  A large scale test dataset to determine optimal retention index threshold based on three mass spectral similarity measures.

Authors:  Jun Zhang; Imhoi Koo; Bing Wang; Qing-Wei Gao; Chun-Hou Zheng; Xiang Zhang
Journal:  J Chromatogr A       Date:  2012-06-19       Impact factor: 4.759

6.  PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.

Authors:  Sean O'Callaghan; David P De Souza; Andrew Isaac; Qiao Wang; Luke Hodkinson; Moshe Olshansky; Tim Erwin; Bill Appelbe; Dedreia L Tull; Ute Roessner; Antony Bacic; Malcolm J McConville; Vladimir A Likić
Journal:  BMC Bioinformatics       Date:  2012-05-30       Impact factor: 3.169

7.  Searching molecular structure databases with tandem mass spectra using CSI:FingerID.

Authors:  Kai Dührkop; Huibin Shen; Marvin Meusel; Juho Rousu; Sebastian Böcker
Journal:  Proc Natl Acad Sci U S A       Date:  2015-09-21       Impact factor: 11.205

Review 8.  Integrating omics technologies to study pulmonary physiology and pathology at the systems level.

Authors:  Ravi Ramesh Pathak; Vrushank Davé
Journal:  Cell Physiol Biochem       Date:  2014-04-28

Review 9.  After the feature presentation: technologies bridging untargeted metabolomics and biology.

Authors:  Kevin Cho; Nathaniel G Mahieu; Stephen L Johnson; Gary J Patti
Journal:  Curr Opin Biotechnol       Date:  2014-05-06       Impact factor: 9.740

Review 10.  Identification of small molecules using accurate mass MS/MS search.

Authors:  Tobias Kind; Hiroshi Tsugawa; Tomas Cajka; Yan Ma; Zijuan Lai; Sajjan S Mehta; Gert Wohlgemuth; Dinesh Kumar Barupal; Megan R Showalter; Masanori Arita; Oliver Fiehn
Journal:  Mass Spectrom Rev       Date:  2017-04-24       Impact factor: 10.946

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.