17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls

      product-review

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Statistical comparison of peptide profiles in biomarker discovery requires fast, user-friendly software for high throughput data analysis. Important features are flexibility in changing input variables and statistical analysis of peptides that are differentially expressed between patient and control groups. In addition, integration the mass spectrometry data with the results of other experiments, such as microarray analysis, and information from other databases requires a central storage of the profile matrix, where protein id's can be added to peptide masses of interest.

          Results

          A new database application is presented, to detect and identify significantly differentially expressed peptides in peptide profiles obtained from body fluids of patient and control groups. The presented modular software is capable of central storage of mass spectra and results in fast analysis. The software architecture consists of 4 pillars, 1) a Graphical User Interface written in Java, 2) a MySQL database, which contains all metadata, such as experiment numbers and sample codes, 3) a FTP (File Transport Protocol) server to store all raw mass spectrometry files and processed data, and 4) the software package R, which is used for modular statistical calculations, such as the Wilcoxon-Mann-Whitney rank sum test. Statistic analysis by the Wilcoxon-Mann-Whitney test in R demonstrates that peptide-profiles of two patient groups 1) breast cancer patients with leptomeningeal metastases and 2) prostate cancer patients in end stage disease can be distinguished from those of control groups.

          Conclusion

          The database application is capable to distinguish patient Matrix Assisted Laser Desorption Ionization (MALDI-TOF) peptide profiles from control groups using large size datasets. The modular architecture of the application makes it possible to adapt the application to handle also large sized data from MS/MS- and Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry experiments. It is expected that the higher resolution and mass accuracy of the FT-ICR mass spectrometry prevents the clustering of peaks of different peptides and allows the identification of differentially expressed proteins from the peptide profiles.

          Related collections

          Most cited references18

          • Record: found
          • Abstract: not found
          • Article: not found

          Individual Comparisons by Ranking Methods

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Curve registration

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules.

              Here a fully automated computer algorithm is applied to complex mass spectra of peptides and proteins. This method uses a subtractive peak finding routine to locate possible isotopic clusters in the spectrum, subjecting these to a combination of the previous Fourier transform/Patterson method for primary charge determination and the method for least-squares fitting to a theoretically derived isotopic abundance distribution for m/z determination of the most abundant isotopic peak, and the statistical reliability of this determination. If a predicted protein sequence is available, each such m/z value is checked for assignment as a sequence fragment. A new signal-to-noise calculation procedure has been devised for accurate determination of baseline and noise width for spectra with high peak density. In 2 h, the program identified 824 isotopic clusters representing 581 mass values in the spectrum of a GluC digest of a 191 kDa protein; this is >50% more than the number of mass values found by the extremely tedious operator-applied methodology used previously. The program should be generally applicable to classes of large molecules, including DNA and polymers. Thorough high resolution analysis of spectra by Horn (THRASH) is proposed as the program's verb.
                Bookmark

                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                2006
                5 September 2006
                : 7
                : 403
                Affiliations
                [1 ]Department of Neurology, Erasmus MC, Dr. Molewaterplein 40, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands
                [2 ]Department of Urology, Erasmus MC, Dr. Molewaterplein 40, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands
                [3 ]FOM-institute for Atomic and Molecular Physics, Kruislaan 407, 1098 SJ Amsterdam, The Netherlands
                Article
                1471-2105-7-403
                10.1186/1471-2105-7-403
                1594579
                16953879
                24760283-af08-40d2-aaac-82f2ff3e192d
                Copyright © 2006 Titulaer et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 April 2006
                : 5 September 2006
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article