Bioinformatische Verfahren zur Analyse von Primärstrukturinformation mittels massenspektrometrischer Daten in der Proteomanalyse

Die für Proteomstudien benötigte automatische Hochdurchsatzidentifikation von Peptidmassenspektren erreicht derzeit nicht einen mit der manuellen Einzelanalyse vergleichbaren Qualitätsstandard. Bei sog. Peptide Mass Fingerprint (PMF)-Datensätzen bedingen unzureichende Präprozessierung der Spektren und uneffektive Evaluation niedrige Identifikationsraten. Auch auf der Ebene von sog. Peptide Fragmentation Fingerprint (PFF)-Daten bleibt eine große Anzahl der MS/MS Spektren unerklärt, selbst nach erfolgreicher Proteinidentifikation. Viele dieser Spektren enthalten wichtige Informationen über die analysierten Proteine (z.B. posttranslationale Modifikationen). Im Rahmen dieser Arbeit wurden verschiedene Algorithmen entwickelt, um die derzeitigen Limitationen in der Interpretation von Massenspektren zu überwinden. Es konnte gezeigt werden, daß durch die Verwendung von geeigneten Algorithmen die Identifikationsrate ungeklärter Peptidmassenspektren deutlich gesteigert werden kann

Interpretation of mass spectrometry data for high-throughput proteomics

Recent developments in proteomics have revealed a bottleneck in bioinformatics: high-quality interpretation of acquired MS data. The ability to generate thousands of MS spectra per day, and the demand for this, makes manual methods inadequate for analysis and underlines the need to transfer the advanced capabilities of an expert human user into sophisticated MS interpretation algorithms. The identification rate in current high-throughput proteomics studies is not only a matter of instrumentation. We present software for high-throughput PMF identification, which enables robust and confident protein identification at higher rates. This has been achieved by automated calibration, peak rejection, and use of a meta search approach which employs various PMF search engines. The automatic calibration consists of a dynamic, spectral information-dependent algorithm, which combines various known calibration methods and iteratively establishes an optimised calibration. The peak rejection algorithm filters signals that are unrelated to the analysed protein by use of automatically generated and dataset-dependent exclusion lists. In the "meta search" several known PMF search engines are triggered and their results are merged by use of a meta score. The significance of the meta score was assessed by simulation of PMF identification with 10,000 artificial spectra resembling a data situation close to the measured dataset. By means of this simulation the meta score is linked to expectation values as a statistical measure. The presented software is part of the proteome database ProteinScape which links the information derived from MS data to other relevant proteomics data. We demonstrate the performance of the presented system with MS data from 1891 PMF spectra. As a result of automatic calibration and peak rejection the identification rate increased from 6% to 44%

Functional annotation of proteins identified in human brain during the HUPO Brain Proteome project pilot study

The HUPO Brain Proteome Project is an initiative coordinating proteomics studies to characterise human and mouse brain proteomes. Proteins identified in human brain samples during the project's pilot phase were put into biological context through integration with various annotation sources followed by a bioinformatics analysis. The data set was related to the genome sequence via the genes encoding identified proteins including an assessment of splice variant identification as well as an analysis of tissue specificity of the respective transcripts. Proteins were furthermore categorised according to subcellular localisation, molecular function and biological process, grouped into protein families and mapped to biological pathways they are known to act in. Involvement in pathological conditions was examined based on association with entries in the online version of Mendelian Inheritance in Man and an interaction network was derived from curated protein-proteininteraction data. Overall a non-redundant set of 1804 proteins was identified in human brain samples. In the majority of cases splice variants could be unambiguously identified by unique peptides, including matches to several hypothetical transcripts of known as well as predicted genes

The HUPO Brain Proteome Project

The proteome analysis started by the Human Proteome Organization (HUPO)1 is the second big international consortium project after the sequencing of the human genome by the Human Genome Project (HUGO)2. The aim of the HUPO Brain Proteome Project (BPP)3 is to derive in depth knowledge of the brain from analysing samples with state-of-the-art proteomics techniques

The HUPO Brain Proteome Project jamboree : centralised summary of the pilot studies.

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On January 9-11, 2006 the members as well as invited analysts came together at the European Bioinformatics Institute in Hinxton, UK for the pilot studies jamboree. The results of the reprocessing were presented and tasks forces were initiated to compile, to interpret and to summarise the data obtained

5th HUPO BPP Bioinformatics Meeting at the European Bioinformatics Institute in Hinxton, UK--Setting the analysis frame.

The Bioinformatics Committee of the HUPO Brain Proteome Project (HUPO BPP) meets regularly to execute the post-lab analyses of the data produced in the HUPO BPP pilot studies. On July 7, 2005 the members came together for the 5th time at the European Bioinformatics Institute (EBI) in Hinxton, UK, hosted by Rolf Apweiler. As a main result, the parameter set of the semi-automated data re-analysis of MS/MS spectra has been elaborated and the subsequent work steps have been defined