Theoretical B-factors for the four systems studied calculated from the simulations.

<p><i>Black lines</i>, B-DNA simulations; <i>red lines</i>, Z-DNA simulations; <i>solid lines</i>, unmethylated DNA; <i>dahsed lines</i>, methylated DNA; <i>circles</i> and <i>tirangles</i>, independent simulation results for each system. Terminal bases not included in the calculatieon. Bases 11 to 20 form the complementary strand.</p

Time evolution of the fraction of BI states for B-DNA and 5mCB-DNA.

<p>The fraction of nucleotides in BI (ε/ζ) conformation are shown as a function of time; <i>black</i>, unmethylated B-DNA; <i>red</i>, methylated 5mCB-DNA; <i>solid</i> and <i>dashed lines</i>, two independent 50 ns simulations. The plots are smoothed using a 500 ps sliding window.</p

Relative free energy profiles across the ε−ζ reaction coordinates in B- and 5mCB-DNA.

<p>The plots show the changes in free energy (y-axis) across the ε−ζ coordinate range (x-axis) that define the BI and BII sub-states. (A) Overall relative free energy profiles for unmethylated B-DNA (<i>black</i>) and methylated 5mCB-DNA (<i>red</i>). (B) Relative free energy profiles for unmethylated and methylated CpG and GpC steps. <i>Black</i>, CpG steps of B-DNA; <i>red</i>, 5mCpG steps of 5mCB-DNA; <i>blue</i>, GpC steps of B-DNA; <i>green</i>, Gp5mC steps of 5mCB-DNA.</p

Scatter plots of α vs. γ for CpG and GpC steps.

<p>(A) GpC steps in B-DNA; (B) CpG steps in B-DNA; (C) Gp5mC steps in 5mCB-DNA; (D) 5mCpG steps in 5mCB-DNA. The plots are color-coded based on the density of points.</p

MM/PBSA analysis of B, methylated B (BM), Z and methylated Z (ZM) DNA.

1<p>SE, standard error.</p>2<p>All energy values are in kcal/mol.</p

Schematics of torsional angles in a nucleotide phosphate backbone.

<p>α and γ angles determine canonical/non-canonical backbone conformations; ε and ζ define BI and BII sub-states of B-DNA.</p

The α/γ distributions in B- and Z-DNA.

<p><b>Landscape of the combined distributions of phosphate torsion angles along the α/γ space.</b> (A) B-DNA; (B) 5mCB-DNA; (C) Z-DNA; (D) 5mCZ-DNA. The plots are color-coded based on the density of points. The results from the two independent simulations for each state are combined, giving 1.6 million points. The color bars on panels B and D show the density values for B- (panels A and B) and Z-DNA (panels C and D) simulations.</p

Query times for batch queries on PubMed abstracts with gene, miRNA and/or cancer term lexicons.

<p>Query times for batch queries on PubMed abstracts with gene, miRNA and/or cancer term lexicons.</p

Growth of articles in MEDLINE.

<p>A bar chart displaying the number of baseline records in NLM MEDLINE’s 2001 baseline release to 2012 baseline release. (<a href="http://www.nlm.nih.gov/bsd/licensee/2012_stats/baseline_doc.html" target="_blank">http://www.nlm.nih.gov/bsd/licensee/2012_stats/baseline_doc.html</a>).</p

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

<div><p>As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.</p></div