Submitted by Juliano Farias da Nobrega null (juliano@e8.com.br) on 2016-04-13T15:21:20Z No. of bitstreams: 1 dissert_juliano_unesp.pdf: 1652677 bytes, checksum: 2d05540d73450af0ce70d07689eeac2a (MD5) / Rejected by Felipe Augusto Arakaki (arakaki@reitoria.unesp.br), reason: Solicitamos que realize uma nova submissão seguindo as orientações abaixo: O arquivo submetido está sem a ficha catalográfica. A versão submetida por você é considerada a versão final da dissertação/tese, portanto não poderá ocorrer qualquer alteração em seu conteúdo após a aprovação. Corrija esta informação e realize uma nova submissão contendo o arquivo correto. Agradecemos a compreensão. on 2016-04-14T20:43:40Z (GMT) / Submitted by Juliano Farias da Nobrega null (juliano@e8.com.br) on 2016-04-15T13:45:15Z No. of bitstreams: 1 Dissertacao_Juliano_Unesp.pdf: 1798501 bytes, checksum: 97b5fd5aa56bbac1dd28b2e73b516bd4 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-04-18T13:22:17Z (GMT) No. of bitstreams: 1 nobrega_jf_me_sjrp.pdf: 1798501 bytes, checksum: 97b5fd5aa56bbac1dd28b2e73b516bd4 (MD5) / Made available in DSpace on 2016-04-18T13:22:17Z (GMT). No. of bitstreams: 1 nobrega_jf_me_sjrp.pdf: 1798501 bytes, checksum: 97b5fd5aa56bbac1dd28b2e73b516bd4 (MD5) Previous issue date: 2016-02-29 / Recentemente, a bioinformática tornou-se um recurso imprescindível para a análise e interpretação da grande quantidade de informação biológica gerada pela biologia molecular e pelos sequenciadores de última geração. O processo de comparação dessas biossequências é o ponto de partida para o estudo da evolução e diferenciação dos organismos vivos, além de ser uma das tarefas mais importantes na biologia computacional. Neste trabalho apresenta-se uma abordagem baseada na heurística de Cadeias de Markov para otimização de um algoritmo de alinhamento múltiplo de sequências biológicas, proporcionando resultados com mais qualidade e sem o comprometimento do desempenho da ferramenta MUSCLE, escolhida para dar suporte ao trabalho. As cadeias de Markov foram escolhidas como técnica de otimização devido sua eficiente aplicabilidade em diversos problemas, sobretudo na biologia computacional, pois sua metodologia probabilística torna a aplicação computacionalmente viável, contornando os problemas NP-difícil e apresentando resultados significamente precisos. / Recently, bioinformatics has become an indispensable tool for analyzing and interpreting large amounts of information biological generated by molecular biology and the next-generation sequencers. The comparison process these sequences is the starting point for the study of evolution and differentiation of living organisms as well as being one of the most important tasks in computational biology. This work presents an approach based on Markov chains heuristics for optimization of a multiple alignment algorithm of biological sequences, provides improved quality results and without compromising the performance of MUSCLE tool chosen to support the work.. Markov chains were chosen as optimization technique due to its efficient applicability in various other problems, especially in computational biology, as its probabilistic methodology makes applying computationally feasible, bypassing the NP-hard problems and stating significantly accurate results.

The ability to tackle NP-hard problems has been greatly extended by the introduction of Metaheuristics (see Blum & Roli (2003)) for a summary of most Metaheuristics, general problem-independent optimisation algorithms extending the hill-climbing local search approach to escape local minima. One of these algorithms is Iterated Local Search (ILS) (Lourenco et al., 2002; Stützle, 1999a, p. 25ff), a recent easy to implement but powerful algorithm with results comparable or superior to other state-of-the-art methods for many combinatorial optimisation problems, among them the Traveling Salesman (TSP) and Quadratic Assignment Problem (QAP). ILS iteratively samples local minima by modifying the current local minimum and restarting a local search porcedure on this modified solution. This thesis will show how ILS can be implemented for MSA. After that, ILS will be evaluated and compared to other MSA algorithms by BAliBASE (Thomson et al., 1999), a set of manually refined alignments used in most recent publications of algorithms and in at least two MSA algorithm surveys. The runtime-behaviour will be evaluated using runtime-distributions. The quality of alignments produced by ILS is at least as good as the best algorithms available and significantly superiour to previously published Metaheuristics for MSA, Tabu Search and Genetic Algorithm (SAGA). On the average, ILS performed best in five out of eight test cases, second for one test set and third for the remaining two. A drawback of all iterative methods for MSA is the long runtime needed to produce good alignments. ILS needs considerably less runtime than Tabu Search and SAGA, but can not compete with progressive or consistency based methods, e. g. ClustalW or T-COFFEE.

Three-dimensional (3D) structure of a protein is essential as the guidance of structure-based drug dis-covery. To achieve robust homology modeling with atomic-level accuracy, reliable loop predictions are required. Here, a novel hierarchical protocol of Protein Local Optimization Program (PLOP) is designed to produce sub-2 angstrom predictions on loop regions in homology modeling. Dramatic improvements in both speed and accuracy have been realized with implementation of special-designed clustering and adaptive loop closure algorithm. Four prediction rounds are designed for homology modeling as the high-level protocol of PLOP, which allows latter rounds employ the educated guess of backbone atom positions and hydrogen bonding information inherited from the previous rounds, contributing to additional prediction accuracy. The success of PLOP has been demonstrated with four different data sets, mainly concen-trating on homology modeling of H3 loops of antibodies. GPU-accelerated sampling algorithm and deep learning models are implemented, which are able to produce promising predictions as input templates for PLOP in the context of homology modeling.

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Residue coupling in protein families has received much attention as an important indicator toward predicting protein structures and revealing functional insight into proteins. Existing coupling methods identify largely pairwise couplings and express couplings over amino acid combinations, which do not yield a mechanistic explanation. Most of these methods primarily use a multiple protein sequence alignment---most likely a resultant alignment---which better exposes couplings and is obtained through manual tweaking of an alignment constructed by a classical alignment algorithm. Classical alignment algorithms primarily focus on capturing conservations and may not fully unveil couplings in the alignment. In this dissertation, we propose methods for capturing both pairwise and higher-order couplings in protein families. Our methods provide mechanistic explanations for couplings using physicochemical properties of amino acids and discernibility between orders. We also investigate a method for mining frequent episodes---called coupled patterns---in an alignment produced by a classical algorithm for proteins and for exploiting the coupled patterns for improving the alignment quality in terms of exposition of couplings. We demonstrate the effectiveness of our proposed methods on a large collection of sequence datasets for protein families. / Ph. D.

With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. Besides comparing performance between multiple runs, also comparing performance characteristics across the parallel execution streams of an application is essential to detect performance problems. This is typically useful to detect imbalances, outliers, or changing runtime behavior during the execution of an application. While such comparisons are straightforward for the aggregated data in performance profiles, only limited solutions exist for comparing event traces. Trace-based analysis, i.e., the collection of fine-grained information on individual application events with timestamps and application context, has proven to be a powerful technique. The detailed performance information included in event traces make them very suitable for performance analysis. However, this level of detail also presents a challenge because it implies a large and overwhelming amount of data. Currently, users need to perform manual comparison of event traces, which is extremely challenging and time consuming because of the large volume of detailed data and the need to correctly line up trace events. To fill the gap of missing solutions for automatic comparison of event traces, this work proposes a set of techniques that automatically align traces. The alignment allows their structural comparison and the highlighting of differences between them. A set of novel metrics provide the user with an objective measure of the differences between traces, both in terms of differences in the event stream and timing differences across events. An additional important aspect of trace-based analysis is the visualization of performance data in event timelines. This has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of event timelines quickly hits the limits of available display resolution. Likewise, identifying performance problems is challenging in the large amount of visualized performance data. To alleviate these problems this work proposes two new approaches for event timeline visualization. First, novel folding strategies for event timelines facilitate visual scalability and provide powerful overviews of performance data at the same time. Second, this work presents an effective approach that automatically identifies and highlights several types of performance critical sections in an application run. This approach identifies time dominant functions of an application and subsequently uses them to analyze runtime imbalances throughout the application run. Intuitive visualizations present the resulting runtime variations and guide the analyst to performance hot spots. Evaluations with benchmarks and real-world applications assess all introduced techniques. The effectiveness of the comparison approaches is demonstrated by showing automatically detected performance issues and structural differences between different versions of applications and across parallel execution streams. Case studies showcase the capabilities of the event timeline visualization techniques by demonstrating scalable performance data visualizations and detecting performance problems and code inefficiencies in real-world applications.

With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. Besides comparing performance between multiple runs, also comparing performance characteristics across the parallel execution streams of an application is essential to detect performance problems. This is typically useful to detect imbalances, outliers, or changing runtime behavior during the execution of an application. While such comparisons are straightforward for the aggregated data in performance profiles, only limited solutions exist for comparing event traces. Trace-based analysis, i.e., the collection of fine-grained information on individual application events with timestamps and application context, has proven to be a powerful technique. The detailed performance information included in event traces make them very suitable for performance analysis. However, this level of detail also presents a challenge because it implies a large and overwhelming amount of data. Currently, users need to perform manual comparison of event traces, which is extremely challenging and time consuming because of the large volume of detailed data and the need to correctly line up trace events. To fill the gap of missing solutions for automatic comparison of event traces, this work proposes a set of techniques that automatically align traces. The alignment allows their structural comparison and the highlighting of differences between them. A set of novel metrics provide the user with an objective measure of the differences between traces, both in terms of differences in the event stream and timing differences across events. An additional important aspect of trace-based analysis is the visualization of performance data in event timelines. This has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of event timelines quickly hits the limits of available display resolution. Likewise, identifying performance problems is challenging in the large amount of visualized performance data. To alleviate these problems this work proposes two new approaches for event timeline visualization. First, novel folding strategies for event timelines facilitate visual scalability and provide powerful overviews of performance data at the same time. Second, this work presents an effective approach that automatically identifies and highlights several types of performance critical sections in an application run. This approach identifies time dominant functions of an application and subsequently uses them to analyze runtime imbalances throughout the application run. Intuitive visualizations present the resulting runtime variations and guide the analyst to performance hot spots. Evaluations with benchmarks and real-world applications assess all introduced techniques. The effectiveness of the comparison approaches is demonstrated by showing automatically detected performance issues and structural differences between different versions of applications and across parallel execution streams. Case studies showcase the capabilities of the event timeline visualization techniques by demonstrating scalable performance data visualizations and detecting performance problems and code inefficiencies in real-world applications.

This thesis summarizes ways of representation of biological sequences and file formats used for sequence exchange and storage. Next part deals with techniques used for sequence pairwise alignment, followed by extension of these techniques to the problem of multiple sequence alignment. Additional methods are introduced, that are suboptimal, but on the other hand are able to compute results in reasonable time. Practical part of this thesis consists of implementing multiple sequence alignment application in Java programming language.