OpenMS – An open-source software framework for mass spectrometry

<p>Abstract</p> <p>Background</p> <p>Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.</p> <p>Results</p> <p>We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.</p> <p>Conclusion</p> <p>OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at <url>http://www.openms.de</url>.</p

Optimal precursor ion selection for LC-MS/MS based proteomics

Shotgun proteomics with Liquid Chromatography (LC) coupled to Tandem Mass Spectrometry (MS/MS) is a key technology for protein identification and quantitation. Protein identification is done indirectly: detected peptide signals are fragmented byMS/MS and their sequence is reconstructed. Afterwards, the identified peptides are used to infer the proteins present in a sample. The problem of choosing the peptide signals that shall be identified with MS/MS is called precursor ion selection. Most workflows use data- dependent acquisition for precursor ion selection despite known drawbacks like data redundancy, limited reproducibility or a bias towards high-abundance proteins. In this thesis, we formulate optimization problems for different aspects of precursor ion selection to overcome these weaknesses. In the first part of this work we develop inclusion lists aiming at optimal precursor ion selection given different input information. We trace precursor ion selection back to known combinatorial problems and develop linear program (LP) formulations. The first method creates an inclusion list given a set of detected features in an LC-MS map. We show that this setting is an instance of the Knapsack Problem. The corresponding LP can be solved efficiently and yields inclusion lists that schedule more precursors than standard methods when the number of precursors per fraction is limited. Furthermore, we develop a method for inclusion list creation based on a list of proteins of interest. We employ retention time and detectability prediction to infer LC-MS features. Based on peptide detectability, we introduce protein detectabilities that reflect the likelihood of detecting and identifying a protein. By maximizing the sum of protein detectabilities we create an inclusion list of limited size that covers a maximum number of proteins. In the second part of the thesis, we focus on iterative precursor ion selection (IPS) with LC-MALDI MS/MS. Here, after a fixed number of acquired MS/MS spectra their identification results are evaluated and are used for the next round of precursor ion selection. We develop a heuristic which creates a ranked precursor list. The second method, IPS LP, is a combination of the two inclusion list scenarios presented in the first part. Additionally, a protein-based exclusion is part of the objective function. For evaluation, we compared both IPS methods to a static inclusion list (SPS) created before the beginning of MS/MS acquisition. We simulated precursor ion selection on three data sets of different complexity and show that IPS LP can identify the same number of proteins with fewer selected precursors. This improvement is especially pronounced for low abundance proteins. Additionally, we show that IPS LP decreases the bias to high abundance proteins. All presented algorithms were implemented in OpenMS, a software library for mass spectrometry. Finally, we present an online tool for IPS that has direct access to the instrument and controls the measurement

Optimale Prekursor-Ionenauswahl für die LC-MS/MS basierte Proteomik

Shotgun proteomics with Liquid Chromatography (LC) coupled to Tandem Mass Spectrometry (MS/MS) is a key technology for protein identification and quantitation. Protein identification is done indirectly: detected peptide signals are fragmented byMS/MS and their sequence is reconstructed. Afterwards, the identified peptides are used to infer the proteins present in a sample. The problem of choosing the peptide signals that shall be identified with MS/MS is called precursor ion selection. Most workflows use data- dependent acquisition for precursor ion selection despite known drawbacks like data redundancy, limited reproducibility or a bias towards high-abundance proteins. In this thesis, we formulate optimization problems for different aspects of precursor ion selection to overcome these weaknesses. In the first part of this work we develop inclusion lists aiming at optimal precursor ion selection given different input information. We trace precursor ion selection back to known combinatorial problems and develop linear program (LP) formulations. The first method creates an inclusion list given a set of detected features in an LC-MS map. We show that this setting is an instance of the Knapsack Problem. The corresponding LP can be solved efficiently and yields inclusion lists that schedule more precursors than standard methods when the number of precursors per fraction is limited. Furthermore, we develop a method for inclusion list creation based on a list of proteins of interest. We employ retention time and detectability prediction to infer LC-MS features. Based on peptide detectability, we introduce protein detectabilities that reflect the likelihood of detecting and identifying a protein. By maximizing the sum of protein detectabilities we create an inclusion list of limited size that covers a maximum number of proteins. In the second part of the thesis, we focus on iterative precursor ion selection (IPS) with LC-MALDI MS/MS. Here, after a fixed number of acquired MS/MS spectra their identification results are evaluated and are used for the next round of precursor ion selection. We develop a heuristic which creates a ranked precursor list. The second method, IPS LP, is a combination of the two inclusion list scenarios presented in the first part. Additionally, a protein-based exclusion is part of the objective function. For evaluation, we compared both IPS methods to a static inclusion list (SPS) created before the beginning of MS/MS acquisition. We simulated precursor ion selection on three data sets of different complexity and show that IPS LP can identify the same number of proteins with fewer selected precursors. This improvement is especially pronounced for low abundance proteins. Additionally, we show that IPS LP decreases the bias to high abundance proteins. All presented algorithms were implemented in OpenMS, a software library for mass spectrometry. Finally, we present an online tool for IPS that has direct access to the instrument and controls the measurement.Flüssigkeitschromatographie (LC) gekoppelt mit Tandemmassenspektrometrie (MS/MS) ist eine Schlüsseltechnologie für die Proteinidentifikation und Quantifizierung in proteomischen Proben. Dabei werden Proteine indirekt identifiziert: detektierte Peptidsignale werden durch MS/MS fragmentiert und anschließend wird die Peptidsequenz rekonstruiert. Über die identifizierten Peptide werden schließlich die Proteine in der Probe identifiziert. Das Problem der Auswahl der Peptidsignale, die über MS/MS sequenziert werden sollen, heißt Precursor-Ionen-Selektion (PS). Die meisten Selektionsverfahren benutzen rein intensitätsbasierte Ansätze – sogenannte Datenabhängige Akquisition (DDA) – trotz bekannter Schwächen wie Datenredundanz, begrenzter Reproduzierbarkeit oder einer Neigung zur Identifikation häufiger Proteine. In dieser Arbeit entwickeln wir für unterschiedliche Aspekte der PS Formulierungen als Optimierungsprobleme mit dem Ziel den bekannten Schwächen entgegenzusteuern. Im ersten Teil der Arbeit werden für unterschiedliche Anfangsinformationen optimale Inklusionslisten erstellt. Dabei führen wir PS auf bekannte kombinatorische Probleme zurück und entwickeln Formulierungen als Lineare Programme (LP) zur Lösung der Probleme. Die erste Methode basiert auf einer Liste von LC-MS-Features. Wir zeigen, dass sich diese Situation auf das Rucksackproblem zurückführen läßt. Das zugehörige LP erstellt effiziente Inklusionslisten, die mehr Precursor enthalten als Standardmethoden, wenn die Anzahl an Precursor-Ionen pro Fraktion begrenzt ist. Außerdem entwickeln wir eine Methode basierend auf einer Liste an zu identifizierenden Proteinsequenzen. Wir benutzen Schätzverfahren für RT und Detektierbarkeit um repräsentative LC-MS-Features für diese Proteine vorherzusagen. Basierend auf der Peptiddetektierbarkeit führen wir eine Proteindetektierbarkeit ein. Indem wir die Summe dieser maximieren, erstellen wir eine größenbeschränkte Inklusionsliste, die eine maximale Anzahl an Proteinen abdeckt. Im zweiten Teil der Arbeit beschäftigen wir uns mit iterativer PS (IPS) mit LC-MALDI MS/MS. Dabei werden nach einer bestimmten Anzahl an aufgenommenen MS/MS- Spektren deren Identifikationsergebnisse ausgewertet und diese zur weiteren PS benutzt. Wir entwickeln einerseits eine Heuristik, die eine priorisierte Inklusionsliste erstellt.Für die zweite Methode, IPS_LP, kombinieren wir die beiden LP-Formulierungen aus dem ersten Teil und erweitern sie um eine proteinbasierte Exklusion. Für die Auswertung vergleichen wir unsere IPS- Methoden mit einer statischen Inklusionsliste (SPS), die vor Beginn der MS/MS- Messung erstellt wurde. Wir simulieren die PS auf drei Datensätzen mit unterschiedlicher Komplexität und zeigen, dass IPS_LP die gleiche Proteinanzahl wie SPS identifiziert, dabei aber weniger MS/MS-Messungen benötigt. Diese Verbesserung wird insbesondere für Proteine mit geringer Abundanz deutlich. Außerdem können wir zeigen, dass die Neigung zur Identifikation häufiger Proteine gesenkt wird. Unsere Algorithmen wurden als Teil von OpenMS, einer Softwarebibliothek für Massenspektrometrie, implementiert. Im letzten Teil stellen wir außerdem ein Onlinetool vor, dass direkten Zugriff auf das Massenspektrometer hat und die Messungen steuert

An Iterative Strategy for Precursor Ion Selection for LC-MS/MS Based Shotgun Proteomics.

Currently, the precursor ion selection strategies in LC-MS mainly choose the most prominent peptide signals for MS/MS analysis. Consequently, high-abundance proteins are identified by MS/MS of many peptides, whereas proteins of lower abundance might elude identification. We present a novel, iterative and result-driven approach for precursor ion selection that significantly increases the efficiency of an MS/MS analysis by decreasing data redundancy and analysis time. By simulating different strategies for precursor ion selection on an existing data set, we compare our method to existing result-driven strategies and evaluate its performance with regard to mass accuracy, database size, and sample complexity

Network integration and modelling of dynamic drug responses at multi-omics levels

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data