Comprehensive Methods of MALDI-TOF Mass Spectrometry Data Analysis

The article presents a review of methods of MALDI-TOF data. There are many steps of mass spectrometry data analysis. It is complex task and it should cover several platforms. It is important to do comprehensive analysis to obtain useful results. The analysis should cover preprocessing and signals analysis, databases searching and peaks classification.В статье представлен обзор методов MALDI-TOF данных с многошаговым спектрометрическим анализом. Эта сложная задача должна охватывать несколько платформ. Чтобы получить полезные результаты, важно сделать всеобъемлющий анализ. Подобный анализ должен охватывать анализ предварительной обработки сигналов и анализ баз данных и поиска пиков классификации

New Statistical Algorithms for the Analysis of Mass Spectrometry Time-Of-Flight Mass Data with Applications in Clinical Diagnostics

Mass spectrometry (MS) based techniques have emerged as a standard forlarge-scale protein analysis. The ongoing progress in terms of more sensitive machines and improved data analysis algorithms led to a constant expansion of its fields of applications. Recently, MS was introduced into clinical proteomics with the prospect of early disease detection using proteomic pattern matching. Analyzing biological samples (e.g. blood) by mass spectrometry generates mass spectra that represent the components (molecules) contained in a sample as masses and their respective relative concentrations. In this work, we are interested in those components that are constant within a group of individuals but differ much between individuals of two distinct groups. These distinguishing components that dependent on a particular medical condition are generally called biomarkers. Since not all biomarkers found by the algorithms are of equal (discriminating) quality we are only interested in a small biomarker subset that - as a combination - can be used as a fingerprint for a disease. Once a fingerprint for a particular disease (or medical condition) is identified, it can be used in clinical diagnostics to classify unknown spectra. In this thesis we have developed new algorithms for automatic extraction of disease specific fingerprints from mass spectrometry data. Special emphasis has been put on designing highly sensitive methods with respect to signal detection. Thanks to our statistically based approach our methods are able to detect signals even below the noise level inherent in data acquired by common MS machines, such as hormones. To provide access to these new classes of algorithms to collaborating groups we have created a web-based analysis platform that provides all necessary interfaces for data transfer, data analysis and result inspection. To prove the platform's practical relevance it has been utilized in several clinical studies two of which are presented in this thesis. In these studies it could be shown that our platform is superior to commercial systems with respect to fingerprint identification. As an outcome of these studies several fingerprints for different cancer types (bladder, kidney, testicle, pancreas, colon and thyroid) have been detected and validated. The clinical partners in fact emphasize that these results would be impossible with a less sensitive analysis tool (such as the currently available systems). In addition to the issue of reliably finding and handling signals in noise we faced the problem to handle very large amounts of data, since an average dataset of an individual is about 2.5 Gigabytes in size and we have data of hundreds to thousands of persons. To cope with these large datasets, we developed a new framework for a heterogeneous (quasi) ad-hoc Grid - an infrastructure that allows to integrate thousands of computing resources (e.g. Desktop Computers, Computing Clusters or specialized hardware, such as IBM's Cell Processor in a Playstation 3)

Quantification and Simulation of Liquid Chromatography-Mass Spectrometry Data

Computational mass spectrometry is a fast evolving field that has attracted increased attention over the last couple of years. The performance of software solutions determines the success of analysis to a great extent. New algorithms are required to reflect new experimental procedures and deal with new instrument generations. One essential component of algorithm development is the validation (as well as comparison) of software on a broad range of data sets. This requires a gold standard (or so-called ground truth), which is usually obtained by manual annotation of a real data set. Comprehensive manually annotated public data sets for mass spectrometry data are labor-intensive to produce and their quality strongly depends on the skill of the human expert. Some parts of the data may even be impossible to annotate due to high levels of noise or other ambiguities. Furthermore, manually annotated data is usually not available for all steps in a typical computational analysis pipeline. We thus developed the most comprehensive simulation software to date, which allows to generate multiple levels of ground truth and features a plethora of settings to reflect experimental conditions and instrument settings. The simulator is used to generate several distinct types of data. The data are subsequently employed to evaluate existing algorithms. Additionally, we employ simulation to determine the influence of instrument attributes and sample complexity on the ability of algorithms to recover information. The results give valuable hints on how to optimize experimental setups. Furthermore, this thesis introduces two quantitative approaches, namely a decharging algorithm based on integer linear programming and a new workflow for identification of differentially expressed proteins for a large in vitro study on toxic compounds. Decharging infers the uncharged mass of a peptide (or protein) by clustering all its charge variants. The latter occur frequently under certain experimental conditions. We employ simulation to show that decharging is robust against missing values even for high complexity data and that the algorithm outperforms other solutions in terms of mass accuracy and run time on real data. The last part of this thesis deals with a new state-of-the-art workflow for protein quantification based on isobaric tags for relative and absolute quantitation (iTRAQ). We devise a new approach to isotope correction, propose an experimental design, introduce new metrics of iTRAQ data quality, and confirm putative properties of iTRAQ data using a novel approach. All tools developed as part of this thesis are implemented in OpenMS, a C++ library for computational mass spectrometry

A Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2006.Includes bibliographical references (leaves 73-85).Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a multi-stage process which compares probabilistic networks generated from mass spectrometry-based data with a mass-based network of protein interactions. In addition, such models can provide insight on features of existing models by identifying relevant proteins. This work finds that the search space of potential proteins can be reduced such that simple antibody-based tests can be used to validate protein identity. This is done with real proteins as a proof of concept. Regarding protein interaction networks, the largest human protein interaction meta-database was created as part of this project, containing over 162,000 interactions. A further contribution is the implementation of the massome network database of mass-based interactions- which is used in the protein identification process.(cont.) This network is explored in terms potential usefulness for protein identification. The framework provides an approach to a number of core issues in proteomics. Besides providing these tools, it yields a novel way to approach statistical signal processing problems in this domain in a way that can be adapted as proteomics-based technologies mature.by Gil Alterovitz.Ph.D

Medical Informatics

Information technology has been revolutionizing the everyday life of the common man, while medical science has been making rapid strides in understanding disease mechanisms, developing diagnostic techniques and effecting successful treatment regimen, even for those cases which would have been classified as a poor prognosis a decade earlier. The confluence of information technology and biomedicine has brought into its ambit additional dimensions of computerized databases for patient conditions, revolutionizing the way health care and patient information is recorded, processed, interpreted and utilized for improving the quality of life. This book consists of seven chapters dealing with the three primary issues of medical information acquisition from a patient's and health care professional's perspective, translational approaches from a researcher's point of view, and finally the application potential as required by the clinicians/physician. The book covers modern issues in Information Technology, Bioinformatics Methods and Clinical Applications. The chapters describe the basic process of acquisition of information in a health system, recent technological developments in biomedicine and the realistic evaluation of medical informatics

Mass spectrometry data mining for cancer detection

Early detection of cancer is crucial for successful intervention strategies. Mass spectrometry-based high throughput proteomics is recognized as a major breakthrough in cancer detection. Many machine learning methods have been used to construct classifiers based on mass spectrometry data for discriminating between cancer stages, yet, the classifiers so constructed generally lack biological interpretability. To better assist clinical uses, a key step is to discover ”biomarker signature profiles”, i.e. combinations of a small number of protein biomarkers strongly discriminating between cancer states. This dissertation introduces two innovative algorithms to automatically search for a signature and to construct a high-performance signature-based classifier for cancer discrimination tasks based on mass spectrometry data, such as data acquired by MALDI or SELDI techniques. Our first algorithm assumes that homogeneous groups of mass spectra can be modeled by (unknown) Gibbs distributions to generate an optimal signature and an associated signature-based classifier by robust log-likelihood analysis; our second algorithm uses a stochastic optimization algorithm to search for two lists of biomarkers, and then constructs a signature-based classifier. To support these two algorithms theoretically, this dissertation also studies the empirical probability distributions of mass spectrometry data and implements the actual fitting of Markov random fields to these high-dimensional distributions. We have validated our two signature discovery algorithms on several mass spectrometry datasets related to ovarian cancer and to colorectal cancer patients groups. For these cancer discrimination tasks, our algorithms have yielded better classification performances than existing machine learning algorithms and in addition,have generated more interpretable explicit signatures.Mathematics, Department o

Analytical Techniques for the Improvement of Mass Spectrometry Protein Profiling

Bioinformatics is rapidly advancing through the "post-genomic" era following the sequencing of the human genome. In preparation for studying the inner workings behind genes, proteins and even smaller biological elements, several subdivisions of bioinformatics have developed. The subdivision of proteomics, concerning the structure and function of proteins, has been aided by the mass spectrometry data source. Biofluid or tissue samples are rapidly assayed for their protein composition. The resulting mass spectra are analyzed using machine learning techniques to discover reliable patterns which discriminate samples from two populations, for example, healthy or diseased, or treatment responders versus non-responders. However, this data source is imperfect and faces several challenges: unwanted variability arising from the data collection process, obtaining a robust discriminative model that generalizes well to future data, and validating a predictive pattern statistically and biologically.This thesis presents several techniques which attempt to intelligently deal with the problems facing each stage of the analytical process. First, an automatic preprocessing method selection system is demonstrated. This system learns from data and selects a combination of preprocessing methods which is most appropriate for the task at hand. This reduces the noise affecting potential predictive patterns. Our results suggest that this method can help adapt to data from different technologies, improving downstream predictive performance. Next, the issues of feature selection and predictive modeling are revisited with respect to the unique challenges posed by proteomic profile data. Approaches to model selection through kernel learning are also investigated. Key insights are obtained for designing the feature selection and predictive modeling portion of the analytical framework. Finally, methods for interpreting the resultsof predictive modeling are demonstrated. These methods are used to assure the user of various desirable properties: validation of the strength of a predictive model, validation of reproducible signal across multiple data generation sessions and generalizability of predictive models to future data. A method for labeling profile features with biological identities is also presented, which aids in the interpretation of the data. Overall, these novel techniques give the protein profiling community additional support and leverage to aid the predictive capability of the technology