Enzyme classification with peptide programs: a comparative study

<p>Abstract</p> <p>Background</p> <p>Efficient and accurate prediction of protein function from sequence is one of the standing problems in Biology. The generalised use of sequence alignments for inferring function promotes the propagation of errors, and there are limits to its applicability. Several machine learning methods have been applied to predict protein function, but they lose much of the information encoded by protein sequences because they need to transform them to obtain data of fixed length.</p> <p>Results</p> <p>We have developed a machine learning methodology, called peptide programs (PPs), to deal directly with protein sequences and compared its performance with that of Support Vector Machines (SVMs) and BLAST in detailed enzyme classification tasks. Overall, the PPs and SVMs had a similar performance in terms of Matthews Correlation Coefficient, but the PPs had generally a higher precision. BLAST performed globally better than both methodologies, but the PPs had better results than BLAST and SVMs for the smaller datasets.</p> <p>Conclusion</p> <p>The higher precision of the PPs in comparison to the SVMs suggests that dealing with sequences is advantageous for detailed protein classification, as precision is essential to avoid annotation errors. The fact that the PPs performed better than BLAST for the smaller datasets demonstrates the potential of the methodology, but the drop in performance observed for the larger datasets indicates that further development is required.</p> <p>Possible strategies to address this issue include partitioning the datasets into smaller subsets and training individual PPs for each subset, or training several PPs for each dataset and combining them using a bagging strategy.</p

Computação paralela na classificação de proteínas sobre a plataforma Cellbe

Tese de mestrado, Informática, Universidade de Lisboa, Faculdade de Ciências, 2010Um dos problemas maiores da bioinformática é a previsão de função de uma proteína. A tecnologia existente já permite obter milhões e milhões de sequências a custo muito reduzido, mas a compreensão de sua função dentro dos vários organismos é ainda um grande mistério para a larga maioria de sequências proteicas existentes. A criação de software eficiente que permita analisar bases de dados de proteínas em busca de metadados é também um desafio para os biólogos e uma área de estudo recente para os cientistas da computação, por representar uma alternativa de baixo custo aos métodos de teste em laboratório. Uma metodologia que se propõe analisar estas bases de dados e anotar as proteínas são os Peptide Programs (PepProg), uma metodologia de aprendizagem automática (machine learning) para classificação funcional de sequências biológicas. Esta dissertação de mestrado se propôs a estudar meios de optimizar o desempenho da implementação existente do método PepProg através da construção de uma implementação alternativa que explore a arquitectura do processador CellBE, presente nas consolas Playstation 3 disponibilizadas pelo Departamento de Informática da Universidade de Lisboa. Para explorar os recursos do CellBE, foi necessário primeiro modificar a implementação existente do método PepProg para que pudesse correr instruções do tipo Single Instruction Multiple Data (SIMD). Esta modificação denominada vectorização, é necessária para que os múltiplos núcleos do processador CellBE pudessem ser utilizados, transformando o PepProg num algoritmo paralelo dentro da arquitectura CellBE. Com o estudo realizado, pode-se concluir que as propostas de vectorização sugeridas nesta dissertação não foram suficientes para a construção de um PepProg paralelo mais eficiente, pois o custo da vectorização do algoritmo em tempo de execução provou-se ser tão alto a ponto de não poder ser compensado pela paralelização do algoritmo.One of the biggest problems on bioinformatics is protein function determination. The existing technology allows the obtaining of millions and millions sequences at very reduced cost, but the understanding of its function inside organisms is still a great mystery for the wide majority of proteomic sequences. The creation of efficient software that allows protein database analyzes in search for metadata is also a challenge for the biologists and an area of recent study for the computation scientists, as it represent a low cost method alternatively to laboratory test methods. A methodology that considers database analysis to write down proteins is the Peptide Programs (PepProg), an automatic learning methodology (machine learning) for functional sorting of biological sequences. This master's degree dissertation studied ways to optimize the existing implementation of the method PepProg by the construction of an alternative implementation that explores CellBE processor architecture, found on Playstation 3 consoles made available by Departamento de Informática of the Universidade de Lisboa. To explore CellBE resources, it was necessary a modification of the existing PepProg implementation to guarantee that only single-instruction multiple-data (SIMD) instructions was present on code. This process, named vectorization, is a prerequisite to allow PepProg program run on CellBE multiple cores in a parallel way. With the accomplished study, it can be concluded that the vectorization methods proposed by this study were not enough for the construction of a more efficient parallel PepProg, because the algorithm vectorization cost in execution time proved to be so high, that the PepProg parallelization gain was not sufficient to compensate the extra computational processing demanded by the vectorization