Marcos Augusto dos Santos

Self-Adaptive Capacity Management for Multi-Tier Virtualized Environments

This paper addresses the problem of hosting multiple applications on a providers virtualized multi-tier infrastructure. Building from a previous model, we design a new self-adaptive capacity management framework, which combines a two-level SLA-driven pricing model, an optimization model and an analytical queuing-based performance model to maximize the providers business objective. Our main contributions are the more accurate multi-queue performance model, which captures application specific bottlenecks and the parallelism inherent to multi-tier platforms, as well as the solution of the extended and much more complex optimization model. Our approach is evaluated via simulation with synthetic as well as realistic workloads, in various scenarios. The results show that our solution is significantly more cost-effective, in terms of the providers achieved revenues, than the approach it is built upon, which uses a single-resource performance model. It also significantly outperforms a multi-tier static allocation strategy for heavy and unbalanced workloads. Finally, preliminary experiments assess the applicability of our framework to virtualized environments subjected to capacity variations caused by the processing of management and security-related tasks.

Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns

BackgroundThe unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75.ResultsCSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level.ConclusionsWe showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.

The heterogeneous HLA genetic composition of the Brazilian population and its relevance to the optimization of hematopoietic stem cell donor recruitment

The aim of this study was to investigate the human leukocyte antigen (HLA) molecular variation across the Brazilian population in order to determine possible regional differences, which would be highly relevant to optimizing donor recruitment strategies in hematopoietic stem cell transplantation (HSCT) and understanding the population genetic background of this heterogeneous country. HLA data of 551 HSCT donors from five Brazilian regions were characterized by high-resolution DNA alleles at the HLA-A, -B, -C, -DRB1 and -DQB1 loci and compared with other populations in Brazil and worldwide populations. Allele and haplotype frequencies were estimated. The analysis was performed to assess Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) among different loci in each recruitment center. Genetic variation was explored through genetic distance analyzed by using a new algorithm based on linear algebra, taking into account geographic regions of Brazil. The results indicated a heterogeneous genetic composition of the Brazilian population, such that HLA allele and haplotype frequencies exhibit different distributions among Brazilian regions, which has important implications for donor matching. In addition, a pronounced differentiation was observed by the absence of clustering of the regional populations in the reduced-dimension space. These data may be useful for increasing donor recruitment with more genetic representativeness in the Brazilian Volunteer Bone Marrow Donors Registry (REDOME).

A singular value decomposition approach for improved taxonomic classification of biological sequences

BackgroundSingular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are not prima facie related. SVD was initially developed to reduce the time needed for information retrieval and analysis of very large data sets in the complex internet environment. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could also facilitate data analysis in this research area.ResultsWe found that SVD applied to amino acid sequences demonstrates relationships and provides a basis for producing clusters and cladograms, demonstrating evolutionary relatedness of species that correlates well with Linnaean taxonomy. The choice of a reasonable number of singular values is crucial for SVD-based studies. We found that fewer singular values are needed to produce biologically significant clusters when SVD is employed. Subsequently, we developed a method to determine the lowest number of singular values and fewest clusters needed to guarantee biological significance; this system was developed and validated by comparison with Linnaean taxonomic classification.ConclusionsBy using SVD, we can reduce uncertainty concerning the appropriate rank value necessary to perform accurate information retrieval analyses. In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.

Description and molecular modeling of four novel HLA‐B alleles identified in Brazilian individuals

Four novel HLA-B alleles, B*42:20, B*51:151, B*57:64 and B*58:42 were identified in Brazilian individuals.

Description of five novel HLA‐B alleles, B*07:184, B*41:27, B*42:19, B*50:32 and B*57:63, identified in Brazilian individuals

Five novel HLA‐B alleles were identified by HLA‐SBT typing in seven unrelated Brazilian individuals. The new alleles discovered include HLA‐B*07:184, B*41:27, B*42:19, B*50:32 and B*57:63 and were officially named by the World Health Organization (WHO) Nomenclature Committee. All new HLA‐B alleles had nonsynonymous nucleotide substitution polymorphisms when compared to their most closely related HLA‐B allele.

Identification of a novel HLA-B allele, B*27:102, in a Brazilian individual.

The HLA-B*27:102 allele may have originated by an intralocus gene conversion event.

Four novel HLA alleles, DRB1*04:11:03, DRB1*10:05, DRB1*15:94 and DRB1*16:22, identified in Brazilian individuals

Four novel human leucocyte antigen (HLA) class II alleles were identified by sequencing‐based typing (SBT) and analysis of the closest‐matching alleles from volunteer subjects from the Brazilian Bone Marrow Donor Register (REDOME, Brazil). The new HLA alleles discovered include DRB1*04:11:03, DRB1*10:05, DRB1*15:94 and DRB1*16:22. Three of the novel alleles had single‐nucleotide substitution polymorphisms when compared to their most homologous allele. Of these, one harboured a single‐nucleotide polymorphism (SNP) identified as a silent substitution.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Genome Visualization in Space

Phylogeny is an important field to understand evolution and the organization of life. However, most methods depend highly on manual study and analysis, making the construction of phylogeny error prone. Linear Algebra methods are known to be efficient to deal with the semantic relationships between a large number of elements in spaces of high dimensionality. Therefore, they can be useful to help the construction of phylogenetic trees. The ability to visualize the relationships between genomes is crucial in this process. In this paper, a linear algebra method, followed by optimization, is used to generate a visualization of a set of complete genomes. Using the proposed method we were able to visualize the relationships of 64 complete mitochondrial genomes, organized as six different groups, and of 31 complete mitochondrial genomes of mammals, organized as nine different groups. The prespecified groups could be seen clustered together in the visualization, and similar species were represented close together. Besides, there seems to be an evolutionary influence in the organization of the graph.