Title

HPLC Retention time prediction for metabolome analysis 

Authors

Takashi Hagiwara¹, Seiji Saito², Yoshifumi Ujiie², Kensaku Imai², Masanori Kakuta¹, Koji Kadota¹, Tohru Terada¹, Kazuya Sumikoshi¹, Kentaro Shimizu^1*, Tatsunari Nishi²

Affiliation

¹Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan; ²Genaris, Inc., Joint Research Center 106, 1-1-40 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan 

Email

shimizu@bi.a.u-tokyo.ac.jp

Article Type

Hypothesis

Date

Received November 17, 2010; Accepted November 24, 2010; Published November 27, 2010
 

Liquid Chromatography Time-of-Flight Mass Spectrometry (LC-TOF-MS) is widely used for profiling metabolite compounds. LC-TOF-MS is a chemical analysis technique that combines the physical separation capabilities of high-pressure liquid chromatography (HPLC) with the mass analysis capabilities of Time-of-Flight Mass Spectrometry (TOF-MS) which utilizes the difference in the flight time of ions due to difference in the mass-to-charge ratio. Since metabolite compounds have various chemical characteristics, their precise identification is a crucial problem of metabolomics research. Contemporaneously analyzed reference standards are commonly required for mass spectral matching and retention time matching, but there are far fewer reference standards than there are compounds in the organism. We therefore developed a retention time prediction method for HPLC to improve the accuracy of identification of metabolite compounds. This method uses a combination of Support Vector Regression and Multiple Linear Regression adaptively to the measured retention time. We achieved a strong correlation (correlation coefficient = 0.974) between measured and predicted retention times for our experimental data. We also demonstrated a successful identification of an E. coli metabolite compound that cannot be identified by precise mass alone. 

Keywords

liquid chromatography; retention time prediction; support vector regression; LC-TOF-MS; metabolomics

Citation

Shimizu et al. Bioinformation, 5(6): 255- 258, 2010

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.