Cooperative Metaheuristics for Exploring Proteomic Data

Most combinatorial optimization problems cannotbe solved exactly. A class of methods, calledmetaheuristics, has proved its efficiency togive good approximated solutions in areasonable time. Cooperative metaheuristics area sub-set of metaheuristics, which implies aparallel exploration of the search space byseveral entities with information exchangebetween them. The importance of informationexchange in the optimization process is relatedto the building block hypothesis ofevolutionary algorithms, which is based onthese two questions: what is the pertinentinformation of a given potential solution andhow this information can be shared? Aclassification of cooperative metaheuristicsmethods depending on the nature of cooperationinvolved is presented and the specificproperties of each class, as well as a way tocombine them, is discussed. Severalimprovements in the field of metaheuristics arealso given. In particular, a method to regulatethe use of classical genetic operators and todefine new more pertinent ones is proposed,taking advantage of a building block structuredrepresentation of the explored space. Ahierarchical approach resting on multiplelevels of cooperative metaheuristics is finallypresented, leading to the definition of acomplete concerted cooperation strategy. Someapplications of these concepts to difficultproteomics problems, including automaticprotein identification, biological motifinference and multiple sequence alignment arepresented. For each application, an innovativemethod based on the cooperation concept isgiven and compared with classical approaches.In the protein identification problem, a firstlevel of cooperation using swarm intelligenceis applied to the comparison of massspectrometric data with biological sequencedatabase, followed by a genetic programmingmethod to discover an optimal scoring function.The multiple sequence alignment problem isdecomposed in three steps involving severalevolutionary processes to infer different kindof biological motifs and a concertedcooperation strategy to build the sequencealignment according to their motif conten

Sparse Proteomics Analysis - A compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets

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)

DEVELOPMENT AND APPLICATION OF MASS SPECTROMETRY-BASED PROTEOMICS TO GENERATE AND NAVIGATE THE PROTEOMES OF THE GENUS POPULUS

Historically, there has been tremendous synergy between biology and analytical technology, such that one drives the development of the other. Over the past two decades, their interrelatedness has catalyzed entirely new experimental approaches and unlocked new types of biological questions, as exemplified by the advancements of the field of mass spectrometry (MS)-based proteomics. MS-based proteomics, which provides a more complete measurement of all the proteins in a cell, has revolutionized a variety of scientific fields, ranging from characterizing proteins expressed by a microorganism to tracking cancer-related biomarkers. Though MS technology has advanced significantly, the analysis of complicated proteomes, such as plants or humans, remains challenging because of the incongruity between the complexity of the biological samples and the analytical techniques available. In this dissertation, analytical methods utilizing state-of-the-art MS instrumentation have been developed to address challenges associated with both qualitative and quantitative characterization of eukaryotic organisms. In particular, these efforts focus on characterizing Populus, a model organism and potential feedstock for bioenergy. The effectiveness of pre-existing MS techniques, initially developed to identify proteins reliably in microbial proteomes, were tested to define the boundaries and characterize the landscape of functional genome expression in Populus. Although these approaches were generally successful, achieving maximal proteome coverage was still limited by a number of factors, including genome complexity, the dynamic range of protein identification, and the abundance of protein variants. To overcome these challenges, improvements were needed in sample preparation, MS instrumentation, and bioinformatics. Optimization of experimental procedures and implementation of current state-of-the-art instrumentation afforded the most detailed look into the predicted proteome space of Populus, offering varying proteome perspectives: 1) network-wide, 2) pathway-specific, and 3) protein-level viewpoints. In addition, we implemented two bioinformatic approaches that were capable of decoding the plasticity of the Populus proteome, facilitating the identification of single amino acid polymorphisms and generating a more accurate profile of protein expression. Though the methods and results presented in this dissertation have direct implications in the study of bioenergy research, more broadly this dissertation focuses on developing techniques to contend with the notorious challenges associated with protein characterization in all eukaryotic organisms

Optimized data processing algorithms for biomarker discovery by LC-MS

This thesis reports techniques and optimization of algorithms to analyse label-free LC-MS data sets for clinical proteomics studies with an emphasis on time alignment algorithms and feature selection methods. The presented work is intended to support ongoing medical and biomarker research. The thesis starts with a review of important steps in a data processing pipeline of label-free Liquid Chromatography – Mass Spectrometry (LC-MS) data. The first part of the thesis discusses an optimization strategy for aligning complex LC-MS chromatograms. It explains the combination of time alignment algorithms (Correlation Optimized Warping, Parametric Time Warping and Dynamic Time Warping) with a Component Detection Algorithm to overcome limitations of the original methods that use Total Ion Chromatograms when applied to highly complex data. A novel reference selection method to facilitate the pre-alignment process and an approach to globally compare the quality of time alignment using overlapping peak area are introduced and used in the study. The second part of this thesis highlights an ongoing challenge faced in the field of biomarker discovery where improvements in instrument resolution coupled with low sample numbers has led to a large discrepancy between the number of measurements and the number of measured variables. A comparative study of various commonly used feature selection methods for tackling this problem is presented. These methods are applied to spiked urine data sets with variable sample size and class separation to mimic typical conditions of biomarker research. Finally, the summary and the remaining challenges in the data processing field are summarized at the end of this thesis.