Metagenômica comparativa de solo de regiões de mata atlântica e caatinga do estado do Rio Grande do Norte - Brasil

The microorganisms play very important roles in maintaining ecosystems, which explains the enormous interest in understanding the relationship between these organisms as well as between them and the environment. It is estimated that the total number of prokaryotic cells on Earth is between 4 and 6 x 1030, constituting an enormous biological and genetic pool to be explored. Although currently only 1% of all this wealth can be cultivated by standard laboratory techniques, metagenomic tools allow access to the genomic potential of environmental samples in a independent culture manner, and in combination with third generation sequencing technologies, the samples coverage become even greater. Soils, in particular, are the major reservoirs of this diversity, and many important environments around us, as the Brazilian biomes Caatinga and Atlantic Forest, are poorly studied. Thus, the genetic material from environmental soil samples of Caatinga and Atlantic Forest biomes were extracted by direct techniques, pyrosequenced, and the sequences generated were analyzed by bioinformatics programs (MEGAN MG-RAST and WEBCarma). Taxonomic comparative profiles of the samples showed that the phyla Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes were the most representative. In addition, fungi of the phylum Ascomycota were identified predominantly in the soil sample from the Atlantic Forest. Metabolic profiles showed that despite the existence of environmental differences, sequences from both samples were similarly placed in the various functional subsystems, indicating no specific habitat functions. This work, a pioneer in taxonomic and metabolic comparative analysis of soil samples from Brazilian biomes, contributes to the knowledge of these complex environmental systems, so far little explored

Biodegradação de hidrocarbonetos aromáticos policíclicos: prospecção metagenômica e modelagem computacional 3-D de proteínas

Knowledge of the native prokaryotes in hazardous locations favors the application of biotechnology for bioremediation. Independent strategies for cultivation and metagenomics contribute to further microbiological knowledge, enabling studies with non-cultivable about the "native microbiological status and its potential role in bioremediation, for example, of polycyclic aromatic hydrocarbons (HPA's). Considering the biome mangrove interface fragile and critical bordering the ocean, this study characterizes the native microbiota mangrove potential biodegradability of HPA's using a biomarker for molecular detection and assessment of bacterial diversity by PCR in areas under the influence of oil companies in the Basin Petroleum Geology Potiguar (BPP). We chose PcaF, a metabolic enzyme, to be the molecular biomarker in a PCR-DGGE detection of prokaryotes that degrade HPA s. The PCR-DGGE fingerprints obtained from Paracuru-CE, Fortim-CE and Areia Branca-RN samples revealed the occurrence of fluctuations of microbial communities according to the sampling periods and in response to the impact of oil. In the analysis of microbial communities interference of the oil industry, in Areia Branca-RN and Paracuru-CE was observed that oil is a determinant of microbial diversity. Fortim-CE probably has no direct influence with the oil activity. In order to obtain data for better understanding the transport and biodegradation of HPA's, there were conducted in silico studies with modeling and simulation from obtaining 3-D models of proteins involved in the degradation of phenanthrene in the transport of HPA's and also getting the 3-D model of the enzyme PcaF used as molecular marker in this study. Were realized docking studies with substrates and products to a better understanding about the transport mechanism and catalysis of HPA s

Análise de cDNAs identificados em bibliotecas substrativas de cDNA para floração de variedades de cana-de-açúcar cultivadas no Rio Grande do Norte(RN): fator de transição NAC, Calmodulina e Fosfatidiltransferase

The flowering is a physiological process that it is vital for plants. This physiological process has been well studied in the plant model Arabidopsis, but in sugarcane this process is not well known. The transition of the shoot apical meristem from vegetative to flowering is a critical factor for plant development. At Brazil northeastern region, the transition to flowering in sugarcane has an important effect as it may reduce up to 60% its production. This is a consequence of the sugar translocation from stalks to the shoot apical meristem which is necessary during the flowering process. Therefore, the aim of this work was to explore and analyze cDNAs previously identified using subtractive cDNA libraries. The results showed that these cDNAs showed differential expression profile in varieties of sugarcane (early x late flowering). The in silico analysis suggested that these cDNAs had homology to calmodulin, NAC transcription factor and phosphatidylinositol, a SEC14, which were described in the literature as having a role in the process of floral development. To better understand the role of the cDNA homologous to calmodulin, tobacco plants were transformed with overexpression cassettes in sense and antissense orientation. Plants overexpressing the cassette in sense orientation did not flowered, while plants overexpressing the cassette in the antissense orientation produced flowers. The data obtained in this study suggested the possible role from CAM sequence, SEC14 and NAC in the induction/floral development pathway in sugarcane, this is the first study in order to analyze these genes in the sugarcane flowering process.

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Um sistema computacional para diagnosticar viroses de plantas usando a técnica de PCR com construção de primers espécie-específicos

It proposes a established computational solution in the development of a software to construct species-specific primers, used to improve the diagnosis of virus of plant for PCR. Primers are indispensable to PCR reaction, besides providing the specificity of the diagnosis. Primer is a synthetic, short, single stranded piece of DNA, used as a starter in PCR technique. It flanks the sequence desired to amplify. Species-specific primers indicate the well known region of beginning and ending where the polymerase enzyme is going to amplify on a certain species, i.e. it is specific for only a species. Thus, the main objective of this work is to automatize the process of choice of primers, optimizing the specificity of chosen primers by the traditional method

Algoritmos de agrupamento aplicados a dados de expressão gênica de câncer: um estudo comparativo

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using classic clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. This work presents the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods

Metagenoma de comunidades microbianas expostas à radiação natural e a metais

Many species have specialized to live in the most varied existing environments showing the remarkable adaptability of the microbial world the most diverse physicochemical conditions. Environments exposed to natural radiation and metals are scarce around the world, presenting a microbiota still unknown. With a total number estimated between 4 and 6 x 1030 microrganisms on earth, they constitute an enormous biological and genetic pool to be explored. Metagenomic approach independent of cultivation, provides a new form to access to the potential genomic environmental samples becoming a powerful tool for the elucidation of ecological functions, metabolic profiles, as well as to identify new biomolecules. In this context, the genetic material of environmental soil and water samples from Açude Boqueirao Parelhas-RN, under the influence of natural radiation and the presence of metals, was extracted, pirosequencing and the generated sequences were analyzed by bioinformatics programs (MG-RAST and STAMP). Taxonomic comparative profiles of both samples showed high abundance of Domain Bacteria, followed by a small portion attributable to Eucaryota Domains, Archaea and Viruses. Proteobacteria, Actinobacteria and Bacterioidetes phyla showed the greater dominance in both samples. Important genera and species associated with resistance to various stressors found in region were observed. Sequences related to oxidative and heat stress, DNA replication and repair, and resistance to toxic compounds were observed, suggesting a significant relationship between the microbiota and their metabolic profile, influenced by regional environmental variables. The results of this study add valuable and unpublished data on the composition of microbial communities in these regions

Caracterização de um homólogo de HINT1 em cana-de-açúcar encontrado em bibliotecas subtrativas de CDNA para floração

The sugarcane is a monocot plant grown in tropical and subtropical regions, with Brazil being the largest producer. Despite its economic importance, little is known about the molecular flowering process in sugarcane. This physiological process can promote a loss up to 60% in sugar or bioethanol. Thus, this work had as objective characterize a HINT1 homologous gene previously identified in subtractive libraries of flowering. Genomic analysis of gene and promoter region structure allowed the observation that there are at least two distinct genes homologous to HINT on sugarcane. Bioinformatics analyses showed the conservation of the characteristic protein domain of HIT superfamily and indicate a phylogenetic relationship associated to cell location. Moreover, a possible relation with the SBTILISIN-like protein family through the information available in interatomas was observed. This suggests that the HINT gene of sugarcane can be related to plant development, there are several possibilities of interactions in the regulation of floral induction process, because the sequences present in regulatory regions indicate that differential expression of HINT was related to with climatic factors in the Northeast region of Brazil as well as to biotic stress and phytohormones. Furthermore, the sugarcane phenotypes indicate that the influence of HINT may happen due to product accumulation of its enzymatic activity. For these characteristics this gene can be used as a marker in the selection of new varieties.

Metagenômica comparativa de solo de regiões de mata atlântica e caatinga do estado do Rio Grande do Norte - Brasil

The microorganisms play very important roles in maintaining ecosystems, which explains the enormous interest in understanding the relationship between these organisms as well as between them and the environment. It is estimated that the total number of prokaryotic cells on Earth is between 4 and 6 x 1030, constituting an enormous biological and genetic pool to be explored. Although currently only 1% of all this wealth can be cultivated by standard laboratory techniques, metagenomic tools allow access to the genomic potential of environmental samples in a independent culture manner, and in combination with third generation sequencing technologies, the samples coverage become even greater. Soils, in particular, are the major reservoirs of this diversity, and many important environments around us, as the Brazilian biomes Caatinga and Atlantic Forest, are poorly studied. Thus, the genetic material from environmental soil samples of Caatinga and Atlantic Forest biomes were extracted by direct techniques, pyrosequenced, and the sequences generated were analyzed by bioinformatics programs (MEGAN MG-RAST and WEBCarma). Taxonomic comparative profiles of the samples showed that the phyla Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes were the most representative. In addition, fungi of the phylum Ascomycota were identified predominantly in the soil sample from the Atlantic Forest. Metabolic profiles showed that despite the existence of environmental differences, sequences from both samples were similarly placed in the various functional subsystems, indicating no specific habitat functions. This work, a pioneer in taxonomic and metabolic comparative analysis of soil samples from Brazilian biomes, contributes to the knowledge of these complex environmental systems, so far little explored