Notification:
We are currently experiencing intermittent issues impacting performance. We apologize for the inconvenience.
By Topic

Automated and rapid bacterial identification using LC-mass spectrometry with a relational database management system

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Deshpande, S.V. ; Science & Technol. Corp., Edgewood, MD, USA ; Jabbour, R.E. ; Wick, C. ; Snyder, A.P.

We have developed an integrated and automated software application for rapid bacterial identification using a relational database management system and liquid chromatography-electrospray-ion trap mass spectrometry (LC-ESl-MS). LC-ESI-MS is used to generate chromatographic profiles of proteins in a bacterial sample along with a software program that automates the data analysis. The software program ProMAPDB automates the data collection, peak identification, spectral purification, mass spectral integration of scans in a peak, and assignment of molecular weights for observed proteins by using a deconvolution algorithm described by Zhang and Marshall. The approach generates a list of biomarker masses along with retention time and relative abundance for all masses obtained by the algorithm. The list of masses is stored in a relational database as a reference library including the sample information such as growth conditions and experimental information. The identification of unknown samples is performed by correlation to the relational database. The bacterial database includes E. coli, Bacillus subtilis, B. thuringiensis, and B. megaterium. The approach has been tested for bacterial discrimination and identification from the mass spectra of mixtures of microorganisms and from mass spectra of organisms at different growth conditions. Experimental factors such as sample preparation, reproducibility, mass range and mass accuracy tolerance are also addressed and evaluated. This approach has the potential for reliable and accurate automated data analysis.

Published in:

Computational Systems Bioinformatics Conference, 2004. CSB 2004. Proceedings. 2004 IEEE

Date of Conference:

16-19 Aug. 2004