Making Common Fund data more findable: catalyzing a data ecosystem.

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data.

Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse.

The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the findable, accessible, interoperable, and reusable (FAIR) form required for reuse is too often deferred to the end of a research project when preparing publications-by which time essential details are no longer accessible.

Rapid Cis-Trans Coevolution Driven by a Novel Gene Retroposed from a Eukaryotic Conserved CCR4-NOT Component in .

Young, or newly evolved, genes arise ubiquitously across the tree of life, and they can rapidly acquire novel functions that influence a diverse array of biological processes. Previous work identified a young regulatory duplicate gene in , that unexpectedly diverged rapidly from its parent, , an extremely conserved component in the CCR4-NOT machinery in post-transcriptional and post-translational regulation of eukaryotic cells, and took on roles in the male reproductive system. This neofunctionalization was accompanied by differential binding of the Zeus protein to loci throughout the   genome.

Integrated paired-end enhancer profiling and whole-genome sequencing reveals recurrent and enhancer hijacking in primary gastric adenocarcinoma.

Objective: Genomic structural variations (SVs) causing rewiring of -regulatory elements remain largely unexplored in gastric cancer (GC). To identify SVs affecting enhancer elements in GC (), we integrated epigenomic enhancer profiles revealed by paired-end H3K27ac ChIP-sequencing from primary GCs with tumour whole-genome sequencing (WGS) data (PeNChIP-seq/WGS).

Design: We applied PeNChIP-seq to 11 primary GCs and matched normal tissues combined with WGS profiles of >200 GCs.

The relation of local order to material properties in relaxor ferroelectrics.

Correlating electromechanical and dielectric properties with nanometre-scale order is the defining challenge for the development of piezoelectric oxides. Current lead (Pb)-based relaxor ferroelectrics can serve as model systems with which to unravel these correlations, but the nature of the local order and its relation to material properties remains controversial. Here we employ recent advances in diffuse scattering instrumentation to investigate crystals that span the phase diagram of PbMgNbO-xPbTiO (PMN-xPT) and identify four forms of local order.

Functional assessment of human enhancer activities using whole-genome STARR-sequencing.

Background: Genome-wide quantification of enhancer activity in the human genome has proven to be a challenging problem. Recent efforts have led to the development of powerful tools for enhancer quantification. However, because of genome size and complexity, these tools have yet to be applied to the whole human genome.

Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales.

Development of Bioinformatics Infrastructure for Genomics Research.

Background: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets.

Background: Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis.

Climate analogues suggest limited potential for intensification of production on current croplands under climate change.

Climate change could pose a major challenge to efforts towards strongly increase food production over the coming decades. However, model simulations of future climate-impacts on crop yields differ substantially in the magnitude and even direction of the projected change. Combining observations of current maximum-attainable yield with climate analogues, we provide a complementary method of assessing the effect of climate change on crop yields.

Predictive Big Data Analytics: A Study of Parkinson's Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations.

Background: A unique archive of Big Data on Parkinson's Disease is collected, managed and disseminated by the Parkinson's Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson's disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style.

Globus Nexus: A Platform-as-a-Service Provider of Research Identity, Profile, and Group Management.

Globus Nexus is a professionally hosted Platform-as-a-Service that provides identity, profile and group management functionality for the research community. Many collaborative e-Science applications need to manage large numbers of user identities, profiles, and groups. However, developing and maintaining such capabilities is often challenging given the complexity of modern security protocols and requirements for scalable, robust, and highly available implementations.

Choosing experiments to accelerate collective discovery.

A scientist's choice of research problem affects his or her personal career trajectory. Scientists' combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency.

Big biomedical data as the key resource for discovery science.

Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments.

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data.

Background: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer.

Experiences Building Globus Genomics: A Next-Generation Sequencing Analysis Service using Galaxy, Globus, and Amazon Web Services.

We describe Globus Genomics, a system that we have developed for rapid analysis of large quantities of next-generation sequencing (NGS) genomic data. This system achieves a high degree of end-to-end automation that encompasses every stage of data analysis including initial data retrieval from remote sequencing centers or storage (via the Globus file transfer system); specification, configuration, and reuse of multi-step processing pipelines (via the Galaxy workflow system); creation of custom Amazon Machine Images and on-demand resource acquisition via a specialized elastic provisioner (on Amazon EC2); and efficient scheduling of these pipelines over many processors (via the HTCondor scheduler). The system allows biomedical researchers to perform rapid analysis of large NGS datasets in a fully automated manner, without software installation or a need for any local computing infrastructure.

Evolution of H3K27me3-marked chromatin is linked to gene expression evolution and to patterns of gene duplication and diversification.

Histone modifications are critical for the regulation of gene expression, cell type specification, and differentiation. However, evolutionary patterns of key modifications that regulate gene expression in differentiating organisms have not been examined. Here we mapped the genomic locations of the repressive mark histone 3 lysine 27 trimethylation (H3K27me3) in four species of Drosophila, and compared these patterns to those in C.

SPOP promotes tumorigenesis by acting as a key regulatory hub in kidney cancer.

Hypoxic stress and hypoxia-inducible factors (HIFs) play important roles in a wide range of tumors. We demonstrate that SPOP, which encodes an E3 ubiquitin ligase component, is a direct transcriptional target of HIFs in clear cell renal cell carcinoma (ccRCC). Furthermore, hypoxia results in cytoplasmic accumulation of SPOP, which is sufficient to induce tumorigenesis.

A spatial modeling framework to evaluate domestic biofuel-induced potential land use changes and emissions.

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO2 emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO2 emissions.

CaGrid Workflow Toolkit: a Taverna based workflow tool for cancer grid.

Background: In biological and medical domain, the use of web services made the data and computation functionality accessible in a unified manner, which helped automate the data pipeline that was previously performed manually. Workflow technology is widely used in the orchestration of multiple services to facilitate in-silico research. Cancer Biomedical Informatics Grid (caBIG) is an information network enabling the sharing of cancer research related resources and caGrid is its underlying service-based computation infrastructure.

A Comparison of Using Taverna and BPEL in Building Scientific Workflows: the case of caGrid.

With the emergence of "service oriented science," the need arises to orchestrate multiple services to facilitate scientific investigation-that is, to create "science workflows." We present here our findings in providing a workflow solution for the caGrid service-based grid infrastructure. We choose BPEL and Taverna as candidates, and compare their usability in the lifecycle of a scientific workflow, including workflow composition, execution, and result analysis.

Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer.

We constructed a large-scale functional network model in Drosophila melanogaster built around two key transcription factors involved in the process of embryonic segmentation. Analysis of the model allowed the identification of a new role for the ubiquitin E3 ligase complex factor SPOP. In Drosophila, the gene encoding SPOP is a target of segmentation transcription factors.

Automatic perceptual color map generation for realistic volume visualization.

Advances in computed tomography imaging technology and inexpensive high performance computer graphics hardware are making high-resolution, full color (24-bit) volume visualizations commonplace. However, many of the color maps used in volume rendering provide questionable value in knowledge representation and are non-perceptual thus biasing data analysis or even obscuring information. These drawbacks, coupled with our need for realistic anatomical volume rendering for teaching and surgical planning, has motivated us to explore the auto-generation of color maps that combine natural colorization with the perceptual discriminating capacity of grayscale.

Genomic analysis of estrogen cascade reveals histone variant H2A.Z associated with breast cancer progression.

We demonstrate an integrated approach to the study of a transcriptional regulatory cascade involved in the progression of breast cancer and we identify a protein associated with disease progression. Using chromatin immunoprecipitation and genome tiling arrays, whole genome mapping of transcription factor-binding sites was combined with gene expression profiling to identify genes involved in the proliferative response to estrogen (E2). Using RNA interference, selected ERalpha and c-MYC gene targets were knocked down to identify mediators of E2-stimulated cell proliferation.