Cloud computing and the DNA data race.

Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Nature Biotechnology (Impact Factor: 32.44). 07/2010; 28(7):691-3. DOI: 10.1038/nbt0710-691
Source: PubMed
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    ABSTRACT: Genome sequencing projects were long confined to biomedical model organisms and required the concerted effort of large consortia. Rapid progress in high-throughput sequencing technology and the simultaneous development of bioinformatic tools have democratized the field. It is now within reach for individual research groups in the eco-evolutionary and conservation community to generate de novo draft genome sequences for any organism of choice. Because of the cost and considerable effort involved in such an endeavour, the important first step is to thoroughly consider whether a genome sequence is necessary for addressing the biological question at hand. Once this decision is taken, a genome project requires careful planning with respect to the organism involved and the intended quality of the genome draft. Here, we briefly review the state of the art within this field and provide a step-by-step introduction to the workflow involved in genome sequencing, assembly and annotation with particular reference to large and complex genomes. This tutorial is targeted at scientists with a background in conservation genetics, but more generally, provides useful practical guidance for researchers engaging in whole-genome sequencing projects.
    Evolutionary Applications 06/2014; · 4.15 Impact Factor
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    ABSTRACT: Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data.
    The Scientific World Journal 04/2014; Volume 2014 (2014)(Article ID 859279):10 pages. · 1.73 Impact Factor
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    ABSTRACT: Stem Cell Antigen-1 (Sca1 or Ly6A/E) is a cell surface marker that is widely expressed in mesenchymal stem cells, including adipose-derived stem cells (ASCs). We hypothesized that the fat depot-specific gene signature of Sca1(high) ASCs may play the major role in defining adipose tissue function and extracellular matrix (ECM) remodeling in a depot-specific manner. Herein we aimed to characterize the unique gene signature and ECM remodeling of Sca1(high) ASCs isolated from subcutaneous (inguinal) and visceral (epididymal) adipose tissues. Sca1(high) ASCs are found in the adventitia and perivascular area of adipose tissues. Sca1(high) ASCs purified with magnetic-activated cell sorting (MACS) demonstrate dendrite or round shape with the higher expression of cytokines and chemokines (e.g., Il6, Cxcl1) and the lower expression of a glucose transporter (Glut1). Subcutaneous and visceral fat-derived Sca1(high) ASCs particularly differ in the gene expressions of adhesion and ECM molecules. While the expression of the major membrane-type collagenase (MMP14) is comparable between the groups, the expressions of secreted collagenases (MMP8 and MMP13) are higher in visceral Sca1(high) ASCs than subcutaneous ASCs. Consistently, slow but focal MMP-dependent collagenolysis was observed with subcutaneous Sca1(high) ASCs, whereas rapid and bulk collagenolysis was observed with visceral Sca1(high) ASCs in MMP-dependent and -independent manners. These results suggest that the fat depot-specific gene signatures of ASCs may contribute to the distinct patterns of ECM remodeling and adipose function in different fat depots.
    Matrix biology: journal of the International Society for Matrix Biology 04/2014; · 3.56 Impact Factor


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