Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Image segmentation using morphological watershed applied to cartography

Image segmentation is a process frequently used in several different areas including Cartography. Feature extraction is a very troublesome task, and successful results require more complex techniques and good quality data. The aims of this paper is to study Digital Image Processing techniques, with emphasis in Mathematical Morphology, to use Remote Sensing imagery, making image segmentation, using morphological operators, mainly the multi-scale morphological gradient operator. In the segmentation process, pre-processing operators of Mathematical Morphology were used, and the multi-scales gradient was implemented to create one of the images used as marker image. Orbital image of the Landsat satellite, sensor TM was used. The MATLAB software was used in the implementation of the routines. With the accomplishment of tests, the performance of the implemented operators was verified and carried through the analysis of the results. The extration of linear feature, using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating. The comparison to the best result obtained was performed by means of the morphology with conventional techniques of features extraction. © Springer-Verlag 2004.

Identificação de genes de predisposição à síndrome dos carcinomas de mama e tireoide por sequenciamento total do exoma

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Técnicas de otimização em alinhamentos múltiplos de sequência via Cadeias de Markov

Pós-graduação em Ciência da Computação - IBILCE

Caracterização do Pythium insidiosum por abordagem proteômica

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estudos experimentais e teórico-computacionais de novos complexos nanozeólitas-enzimas empregados na produção de biocombustíveis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Um novo protocolo in silico para a predição de complexos flexíveis entre proteínas: estudo de caso para inibidores plasmáticos de fosfolipase A2 de serpentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Regulatory variation in a TBX5 enhancer leads to isolated congenital heart disease

Recent studies have identified the genetic underpinnings of a growing number of diseases through targeted exome sequencing. However, this strategy ignores the large component of the genome that does not code for proteins, but is nonetheless biologically functional. To address the possible involvement of regulatory variation in congenital heart diseases (CHDs), we searched for regulatory mutations impacting the activity of TBX5, a dosage-dependent transcription factor with well-defined roles in the heart and limb development that has been associated with the HoltOram syndrome (hearthand syndrome), a condition that affects 1/100 000 newborns. Using a combination of genomics, bioinformatics and mouse genetic engineering, we scanned approximate to 700 kb of the TBX5 locus in search of cis-regulatory elements. We uncovered three enhancers that collectively recapitulate the endogenous expression pattern of TBX5 in the developing heart. We re-sequenced these enhancer elements in a cohort of non-syndromic patients with isolated atrial and/or ventricular septal defects, the predominant cardiac defects of the HoltOram syndrome, and identified a patient with a homozygous mutation in an enhancer approximate to 90 kb downstream of TBX5. Notably, we demonstrate that this single-base-pair mutation abrogates the ability of the enhancer to drive expression within the heart in vivo using both mouse and zebrafish transgenic models. Given the population-wide frequency of this variant, we estimate that 1/100 000 individuals would be homozygous for this variant, highlighting that a significant number of CHD associated with TBX5 dysfunction might arise from non-coding mutations in TBX5 heart enhancers, effectively decoupling the heart and hand phenotypes of the HoltOram syndrome.

A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data

In [1], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined parameter in a way that the clustering stage can be implemented more accurately while having reduced computational complexity

In silico identification of conserved intercoding sequences in Leishmania genomes: Unraveling putative cis-regulatory elements

In silico analyses of Leishmania spp. genome data are a powerful resource to improve the understanding of these pathogens' biology. Trypanosomatids such as Leishmania spp. have their protein-coding genes grouped in long polycistronic units of functionally unrelated genes. The control of gene expression happens by a variety of posttranscriptional mechanisms. The high degree of synteny among Leishmania species is accompanied by highly conserved coding sequences (CDS) and poorly conserved intercoding untranslated sequences. To identify the elements involved in the control of gene expression, we conducted an in silico investigation to find conserved intercoding sequences (CICS) in the genomes of L major, L infantum, and L braziliensis. We used a combination of computational tools, such as Linux-Shell, PERL and R languages, BLAST, MSPcrunch, SSAKE, and Pred-A-Term algorithms to construct a pipeline which was able to: (i) search for conservation in target-regions, (ii) eliminate CICS redundancy and mask repeat elements, (iii) predict the mRNA's extremities, (iv) analyze the distribution of orthologous genes within the generated LeishCICS-clusters, (v) assign GO terms to the LeishCICS-clusters. and (vi) provide statistical support for the gene-enrichment annotation. We associated the LeishCICS-cluster data, generated at the end of the pipeline, with the expression profile oft. donovani genes during promastigote-amastigote differentiation, as previously evaluated by others (GEO accession: GSE21936). A Pearson's correlation coefficient greater than 0.5 was observed for 730 LeishCICS-clusters containing from 2 to 17 genes. The designed computational pipeline is a useful tool and its application identified potential regulatory cis elements and putative regulons in Leishmania. (C) 2012 Elsevier B.V. All rights reserved.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

SIS: a program to generate draft genome sequence scaffolds for prokaryotes

Background: Decreasing costs of DNA sequencing have made prokaryotic draft genome sequences increasingly common. A contig scaffold is an ordering of contigs in the correct orientation. A scaffold can help genome comparisons and guide gap closure efforts. One popular technique for obtaining contig scaffolds is to map contigs onto a reference genome. However, rearrangements that may exist between the query and reference genomes may result in incorrect scaffolds, if these rearrangements are not taken into account. Large-scale inversions are common rearrangement events in prokaryotic genomes. Even in draft genomes it is possible to detect the presence of inversions given sufficient sequencing coverage and a sufficiently close reference genome. Results: We present a linear-time algorithm that can generate a set of contig scaffolds for a draft genome sequence represented in contigs given a reference genome. The algorithm is aimed at prokaryotic genomes and relies on the presence of matching sequence patterns between the query and reference genomes that can be interpreted as the result of large-scale inversions; we call these patterns inversion signatures. Our algorithm is capable of correctly generating a scaffold if at least one member of every inversion signature pair is present in contigs and no inversion signatures have been overwritten in evolution. The algorithm is also capable of generating scaffolds in the presence of any kind of inversion, even though in this general case there is no guarantee that all scaffolds in the scaffold set will be correct. We compare the performance of SIS, the program that implements the algorithm, to seven other scaffold-generating programs. The results of our tests show that SIS has overall better performance. Conclusions: SIS is a new easy-to-use tool to generate contig scaffolds, available both as stand-alone and as a web server. The good performance of SIS in our tests adds evidence that large-scale inversions are widespread in prokaryotic genomes.

Strain-specific polyketide synthase genes of Aspergillus niger

In silico comparison of 34 putative pks genes in Aspergillus niger strain CBS 513.88 versus A. niger strain ATCC 1015 genome revealed significant nucleotide identity (>95% covering a minimum of 99% of the gene sequence) for 31 of these genes (approximately 91%). A. niger CBS 513.88 harbors three putative pks genes (An01g01130, An11g05940, and An15g07920), for which nucleotide identity was not found in A. niger ATCC 1015. To compare the results of the in silico analysis with the in vivo situation, experimental data were obtained for a large number of A. niger strains obtained from different substrates and geographical regions. Three putative Os genes that were found to be variable between the two A. niger strains using bioinformatics tools were in fact strain-specific genes based on experimental data. The PCR amplification signals for the An01g01130, An11g05940, and An15g07920 pks genes were detected in only 97%, 71%, and 26% of the strains, respectively. Southern blot analyses confirmed the PCR data. Because one of the strain-specific pits genes (An15g07920) is located in a putative ochratoxin cluster, we focused our investigation on that region. We assessed the ochratoxin production capability of the 119 A. niger strains and found a positive association between the presence of this pia gene and the capability of the respective strain to produce ochratoxin. (C) 2012 Elsevier B.V. All rights reserved.

Mapping the biosphere: exploring species to understand the origin, organization and sustainability of biodiversity

The time is ripe for a comprehensive mission to explore and document Earth's species. This calls for a campaign to educate and inspire the next generation of professional and citizen species explorers, investments in cyber-infrastructure and collections to meet the unique needs of the producers and consumers of taxonomic information, and the formation and coordination of a multi-institutional, international, transdisciplinary community of researchers, scholars and engineers with the shared objective of creating a comprehensive inventory of species and detailed map of the biosphere. We conclude that an ambitious goal to describe 10 million species in less than 50 years is attainable based on the strength of 250 years of progress, worldwide collections, existing experts, technological innovation and collaborative teamwork. Existing digitization projects are overcoming obstacles of the past, facilitating collaboration and mobilizing literature, data, images and specimens through cyber technologies. Charting the biosphere is enormously complex, yet necessary expertise can be found through partnerships with engineers, information scientists, sociologists, ecologists, climate scientists, conservation biologists, industrial project managers and taxon specialists, from agrostologists to zoophytologists. Benefits to society of the proposed mission would be profound, immediate and enduring, from detection of early responses of flora and fauna to climate change to opening access to evolutionary designs for solutions to countless practical problems. The impacts on the biodiversity, environmental and evolutionary sciences would be transformative, from ecosystem models calibrated in detail to comprehensive understanding of the origin and evolution of life over its 3.8 billion year history. The resultant cyber-enabled taxonomy, or cybertaxonomy, would open access to biodiversity data to developing nations, assure access to reliable data about species, and change how scientists and citizens alike access, use and think about biological diversity information.

The SUCEST-FUN regulatory network database: Designing an energy grass

Modern sugarcane cultivars are complex hybrids resulting from crosses among several Saccharum species. Traditional breeding methods have been employed extensively in different countries over the past decades to develop varieties with increased sucrose yield and resistance to pests and diseases. Conventional variety improvement, however, may be limited by the narrow pool of suitable genes. Thus, molecular genetics is seen as a promising tool to assist in the process of developing improved varieties. The SUCEST-FUN Project (http://sucest-fun.org) aims to associate function with sugarcane genes using a variety of tools, in particular those that enable the study of the sugarcane transcriptome. An extensive analysis has been conducted to characterise, phenotypically, sugarcane genotypes with regard to their sucrose content, biomass and drought responses. Through the analysis of different cultivars, genes associated with sucrose content, yield, lignin and drought have been identified. Currently, tools are being developed to determine signalling and regulatory networks in grasses, and to sequence the sugarcane genome, as well as to identify sugarcane promoters. This is being implemented through the SUCEST-FUN (http://sucest-fun.org) and GRASSIUS databases (http://grassius.org), the cloning of sugarcane promoters, the identification of cis-regulatory elements (CRE) using Chromatin Immunoprecipitation-sequencing (ChIP-Seq) and the generation of a comprehensive Signal Transduction and Transcription gene catalogue (SUCAST Catalogue).