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Prlic A.,University of California at San Diego | Yates A.,European Bioinformatics Institute | Bliven S.E.,University of California at San Diego | Rose P.W.,University of California at San Diego | And 12 more authors.
Bioinformatics | Year: 2012

Motivation: BioJava is an open-source project for processing of biological data in the Java programming language. We have recently released a new version (3.0.5), which is a major update to the code base that greatly extends its functionality.Results: BioJava now consists of several independent modules that provide state-of-the-art tools for protein structure comparison, pairwise and multiple sequence alignments, working with DNA and protein sequences, analysis of amino acid properties, detection of protein modifications and prediction of disordered regions in proteins as well as parsers for common file formats using a biologically meaningful data model. © The Author 2012. Published by Oxford University Press. All rights reserved.

PubMed | University of Trento, King Abdulaziz University, Karolinska Institutet, The Jackson Laboratory and 29 more.
Type: Journal Article | Journal: Nucleic acids research | Year: 2015

The BioMart Community Portal ( is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

News Article | November 23, 2016
Site: adds “Precision Medicine Market Size By Technology (Big Data Analytics, Gene Sequencing, Drug Discovery, Bioinformatics, Companion Diagnostics), By Application (Oncology, CNS, Immunology, Respiratory), Industry Analysis Report, Regional Outlook (U.S., Canada, Germany, UK, France, Scandinavia, Italy, Japan, China, India, Singapore, Mexico, Brazil, South Africa, UAE, Qatar, Saudi Arabia), Application Potential, Price Trends, Competitive Market Share & Forecast, 2016-2023” new report to its research database. The report spread across 94 pages with table and figures in it. Global Precision Medicine Market size was more than $39.1 billion for 2015 and is predicted to register 10.51% of CAGR during forecast timeframe. It is innovative procedure for treating and preventing chronic ailments depending upon changes in individual genes and other lifestyle features. New approach helps doctors properly assess ailment risk and predict optimal treatment. Growing occurrence of cancer and increase in cancer prone geriatric population all across the globe is predicted to boost industry expansion. Threats related with sharing of patients genetic information can hinder industry growth. Insurance firms can use patient data and raise their premium for people who are at a risk of acquiring inherited diseases. Further, decline in rate of FDA (U.S. Food and Drug Administration) drug approval has minimized the rate of production of new medicines in spite of heavy investments. This aspect can hinder global precision medicine market expansion. Technology Trends The industry is segmented into different technologies like gene sequencing, companion diagnostics, big data analytics, bioinformatics and drug discovery. Gene sequencing segment size was more than $8.1 billion for 2015. Current FDA guidelines on next -generation sequencing dependent tests takes into consideration individual differences in genes of various persons, environments and life patterns while creating new type of healthcare. Companion diagnostics segment has acquired importance owing to rising concerns about rates of drug failures. Further, the segment is expanding at rapid pace owing to rise in financial support and approvals by government. Heavy throughput omics techniques applied in biological and basic research are predicted to propel bioinformatics segment growth. Out of all omics techniques next-generation technique is predicted to create key impact on the segment growth. Drug discovery technique contributed more than $9 billion for 2015 and is predicted to register CAGR of 8.31% during forecast timeframe. Further, biomarker directed treatments with medicine targeting epidermal growth factor receptor (EGFR),c-ros oncogene 1 receptor tyrosine kinase (ROS1) and anaplastic lymphoma kinase (ALK) have speeded up the production of new medicines. Precision Medicine Market Application Trends Global industry is segmented into various applications like respiratory application, oncology application, Immunology application and central nervous system (CNS) application. Oncology application contributed more than 30.1% of precision medicine market share for 2015 and is predicted to record CAGR of 10.91% during forecast timeframe. CNS application contributed more than $9.1 billion for 2015. Neuroscience therapeutics has been utilizing the approach for long duration. Regional Trends Global industry was segmented into key geographical regions like North America, MEA, Europe, APAC and Latin America. U.S. precision medicine market share was about 65.1% of revenue of North America. Factors like large allocation of budget by U.S. president to agencies like FDA( U.S. food and drug administration) , NIH (National Institute of Health) and NCI (National Cancer Institute) along with favorable government rules have contributed to the regional industry growth. Germany precision medicine market share was more than $2.5 billion for 2015 and is predicted to contribute significantly to the growth of European industry. Reason for industry growth in the region can be credited to the fact that many institutions have acquired biomarker analysis certification required for colorectal cancer detection tests. Further, medicine producing and diagnostic firms are making tremendous efforts for enhancing industry growth in Europe. Favorable compensation policies are predicted to promote industry growth in France. China contributed more than 25.1% to APAC precision medicine market share for 2015 and is predicted to remain key region in future. Favorable government initiatives and high contributions from academic labs has assisted in the regional industry growth. Competitive Trends Key industry players profiled in the report include Roche Holdings AG, Qiagen, Pfizer, Medtronic, Source Precision Medicine Incorporation, Silicon Biosystems, Tepnel Pharma Services, Covance, Biocrates Life Sciences AG, Novartis, Nanostring Technologies, Laboratory Corporation of America Holdings, Quest Diagnostics, Teva Pharmaceuticals, Intomics, Ferrer InCode, Eagle Genomics Limited and Quest Diagnostics. To receive personalized assistance, write to us @ [email protected] with the report title in the subject line along with your questions or call us at +1 866-764-2150

Chen C.,Cornell University | DeClerck G.,Cornell University | Tian F.,China Agricultural University | Spooner W.,Eagle Genomics | And 3 more authors.
PLoS ONE | Year: 2012

PICARA is an analytical pipeline designed to systematically summarize observed SNP/trait associations identified by genome wide association studies (GWAS) and to identify candidate genes involved in the regulation of complex trait variation. The pipeline provides probabilistic inference about a priori candidate genes using integrated information derived from genome-wide association signals, gene homology, and curated gene sets embedded in pathway descriptions. In this paper, we demonstrate the performance of PICARA using data for flowering time variation in maize - a key trait for geographical and seasonal adaption of plants. Among 406 curated flowering time-related genes from Arabidopsis, we identify 61 orthologs in maize that are significantly enriched for GWAS SNP signals, including key regulators such as FT (Flowering Locus T) and GI (GIGANTEA), and genes centered in the Arabidopsis circadian pathway, including TOC1 (Timing of CAB Expression 1) and LHY (Late Elongated Hypocotyl). In addition, we discover a regulatory feature that is characteristic of these a priori flowering time candidates in maize. This new probabilistic analytical pipeline helps researchers infer the functional significance of candidate genes associated with complex traits and helps guide future experiments by providing statistical support for gene candidates based on the integration of heterogeneous biological information.

Agency: Cordis | Branch: FP7 | Program: CP-FP | Phase: HEALTH.2013.2.4.2-1 | Award Amount: 8.33M | Year: 2014

Asymptomatic vascular damage accumulates for years before patients are identified and subjected to therapeutic measures. The limited knowledge on early vascular disease pathophysiology is reflected in the lack of therapeutic options. SysVasc aims to overcome this limitation by mounting a comprehensive systems medicine approach to elucidate pathological mechanisms, which will yield molecular targets for therapeutic intervention. The consortium is based on established multidisciplinary European research networks, including specialists in pre-clinical and clinical research, omics technologies, and systems biology from research intensive SMEs and academia; partners synergistically provide access to an extensive number of selected population-based cohorts and associated datasets, cutting edge modeling and simulation methods, and established cardiovascular disease (CVD) animal models and patient cohorts. The coordinated application of these tools and know-how will identify pathophysiological mechanisms and key molecules responsible for onset and progression of CVD and validate their potential to serve as molecular targets for therapeutic intervention. To this end, the consortium will also use unique resources to evaluate molecular homology between the available model systems and human disease, which will yield reliable essential preclinical research tools to explore proof of concepts for therapeutic intervention studies and ultimately translate relevant results into novel therapeutic approaches. Collectively, SysVasc will identify and validate novel biology-driven key molecular targets for CVD treatment. Major scientific, societal and economic impact is expected including, but not limited to, providing a valuable resource to further CVD research, and enhance competitiveness of participating SMEs and European health industry in general by translating knowledge into innovative services in therapeutic target and drug research.

PubMed | European Bioinformatics Institute, Wellcome Trust Sanger Institute, Eagle Genomics and University College London
Type: | Journal: Database : the journal of biological databases and curation | Year: 2016

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL:

PubMed | University College Dublin, Eagle Genomics, French Institute of Health and Medical Research and Mosaiques Diagnostics GmbH
Type: Journal Article | Journal: BMC bioinformatics | Year: 2016

When combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects.Computing the discovery sub-cohorts comprising [Formula: see text] of the study subjects based on the Wilcoxon rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largely different numbers (ranging from 2 to 398) of potential peptide biomarkers. Moreover, these biomarker patterns showed very little overlap limited to fragments of type I and III collagens as the common denominator. However, these differences in biomarker patterns did mostly not translate into significant differently performing diagnostic or prognostic classifiers modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and random forest. This was even true when different biomarker patterns were combined into master-patterns.In conclusion, our study revealed a very considerable dependence of peptide biomarker discovery on statistical computing of urinary peptide profiles while the observed diagnostic and/or prognostic reliability of classifiers was widely independent of the modelling approach. This may however be due to the limited statistical power in classifier testing. Nonetheless, our study showed that urinary proteome analysis has the potential to provide valuable biomarkers for coronary artery disease mirroring especially alterations in the extracellular matrix. It further showed that for a comprehensive discovery of biomarkers and thus of pathological information, the results of different statistical methods may best be combined into a master pattern that then can be used for classifier modelling.

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