The Discovery of Putative Urine Markers for the Specific Detection of Prostate Tumor by Integrative Mining of Public Genomic Profiles

Urine has emerged as an attractive biofluid for the noninvasive detection of prostate cancer (PCa). There is a strong imperative to discover candidate urinary markers for the clinical diagnosis and prognosis of PCa. The rising flood of various omics profiles presents immense opportunities for the identification of prospective biomarkers. Here we present a simple and efficient strategy to derive candidate urine markers for prostate tumor by mining cancer genomic profiles from public databases. Prostate, bladder and kidney are three major tissues from which cellular matters could be released into urine. To identify urinary markers specific for PCa, upregulated entities that might be shed in exosomes of bladder cancer and kidney cancer are first excluded. Through the ontology-based filtering and further assessment, a reduced list of 19 entities encoding urinary proteins was derived as putative PCa markers. Among them, we have found 10 entities closely associated with the process of tumor cell growth and development by pathway enrichment analysis. Further, using the 10 entities as seeds, we have constructed a protein-protein interaction (PPI) subnetwork and suggested a few urine markers as preferred prognostic markers to monitor the invasion and progression of PCa. Our approach is amenable to discover and prioritize potential markers present in a variety of body fluids for a spectrum of human diseases

BioInfer: a corpus for information extraction in the biomedical domain

BACKGROUND: Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. RESULTS: We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. CONCLUSION: We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at

Emerging biotechnologies: bioinformatics services applied to agriculture.

Abstract - Bioinformatics is an emergent biotechnological field of study marked by interdisciplinarity and complexity. It involves the application and development of computational tools to biological data in order to process, generate, and disseminate biological knowledge. Bioinformatics is characterized by an intense generation of data and information (configured as a context of big data and e-science), associated with the need for computational resources with high processing and storage capacities and highly qualified and interdisciplinary staff, often found only in academia. The objective of this paper is to describe the organizational model and collaborative innovation activities of the Bioinformatics Multi-user Laboratory (LMB, in the acronym in Portuguese). The LMB is a facility located at the Brazilian Agricultural Research Corporation (Embrapa), the main Brazilian agricultural research public institute, formed by 46 Research and Service Centers distributed throughout Brazil and by several laboratories and business offices abroad, in America, Africa, Asia and Europe. Its mission involves to contribute to the advance of the frontier of knowledge in bioinformatics by: incorporating new technologies and enabling efficient solutions to the demands related to this field; providing access to high performance computing infrastructure and developing human skills. Considering the importance of biotechnology in the context of agricultural research, Embrapa implemented the LMB in 2011, with the purpose of increasing the efficiency of the use of computational, human and technological resources of Embrapa by providing access to bioinformatics computational resources, offering research collaboration possibilities and consultation on project design and biological data analysis. A case-study was conducted based on documentary research and interviews. The main findings of this research are: the description of the organizational model of LMB, the management team and roles; theservices it provides; its access policies and procedures of customer service.Altec 2015

Why Is There a Lack of Consensus on Molecular Subgroups of Glioblastoma? Understanding the Nature of Biological and Statistical Variability in Glioblastoma Expression Data

Gene expression patterns characterizing clinically-relevant molecular subgroups of glioblastoma are difficult to reproduce. We suspect a combination of biological and analytic factors confounds interpretation of glioblastoma expression data. We seek to clarify the nature and relative contributions of these factors, to focus additional investigations, and to improve the accuracy and consistency of translational glioblastoma analyses.We analyzed gene expression and clinical data for 340 glioblastomas in The Cancer Genome Atlas (TCGA). We developed a logic model to analyze potential sources of biological, technical, and analytic variability and used standard linear classifiers and linear dimensional reduction algorithms to investigate the nature and relative contributions of each factor.Commonly-described sources of classification error, including individual sample characteristics, batch effects, and analytic and technical noise make measurable but proportionally minor contributions to inconsistent molecular classification. Our analysis suggests that three, previously underappreciated factors may account for a larger fraction of classification errors: inherent non-linear/non-orthogonal relationships among the genes used in conjunction with classification algorithms that assume linearity; skewed data distributions assumed to be Gaussian; and biologic variability (noise) among tumors, of which we propose three types.Our analysis of the TCGA data demonstrates a contributory role for technical factors in molecular classification inconsistencies in glioblastoma but also suggests that biological variability, abnormal data distribution, and non-linear relationships among genes may be responsible for a proportionally larger component of classification error. These findings may have important implications for both glioblastoma research and for translational application of other large-volume biological databases

Disarmament in the context of the international economic order

This paper focuses on an economic aspect of the disarmament question: the international market of armaments. I shall explore the thesis that arms trade is an increasingly important factor in North-South economic relations, that it affects not only international trade patterns, but also through trade, domestic patterns of economic development. Research assistance for this work was provided by Michael de Mello at Columbia University. In addition to the statistical sources in the references I shall be drawing as well on the results of a UNITAR study on technology, domestic distribution and North-South relations. (1)disarmament; armament; international market; international disarmament; arms trade; arms market; north; south; North-South; international trade; international trade patterns; domestic patterns of economic development; development; domestic economy; economic development; UNITAR; domestic distribution; technology; economic order; international economic order; export; import; commodity trade; trade patterns;

The FunGenES Database: A Genomics Resource for Mouse Embryonic Stem Cell Differentiation

Embryonic stem (ES) cells have high self-renewal capacity and the potential to differentiate into a large variety of cell types. To investigate gene networks operating in pluripotent ES cells and their derivatives, the “Functional Genomics in Embryonic Stem Cells” consortium (FunGenES) has analyzed the transcriptome of mouse ES cells in eleven diverse settings representing sixty-seven experimental conditions. To better illustrate gene expression profiles in mouse ES cells, we have organized the results in an interactive database with a number of features and tools. Specifically, we have generated clusters of transcripts that behave the same way under the entire spectrum of the sixty-seven experimental conditions; we have assembled genes in groups according to their time of expression during successive days of ES cell differentiation; we have included expression profiles of specific gene classes such as transcription regulatory factors and Expressed Sequence Tags; transcripts have been arranged in “Expression Waves” and juxtaposed to genes with opposite or complementary expression patterns; we have designed search engines to display the expression profile of any transcript during ES cell differentiation; gene expression data have been organized in animated graphs of KEGG signaling and metabolic pathways; and finally, we have incorporated advanced functional annotations for individual genes or gene clusters of interest and links to microarray and genomic resources. The FunGenES database provides a comprehensive resource for studies into the biology of ES cells