In silico analysis of cancer through the Cancer Genome Anatomy Project.

The Cancer Genome Anatomy Project (CGAP) was designed and implemented to provide public datasets, material resources and informatics tools to serve as a platform to support the elucidation of the molecular signatures of cancer. This overview of CGAP describes the status of this effort to develop resources based on gene expression, polymorphism identification and chromosome aberrations, and we describe a variety of analytical tools designed to facilitate in silico analysis of these datasets.     

Methods for facilitating web-based participatory research informatics

Scientific datasets are often generated and used by “communities”, or groups of users and creators. These communities, today often virtual, can include researchers, decision-makers, and the general public; participants in these communities may contribute different components during various iterations in the data generation process, and have different needs for resulting data products. As this “participatory research informatics” model, which includes elements of collaborative research, public participation, and digital data libraries, becomes the norm, new approaches to dynamic data management will be important. Researchers commonly use the web as a medium for research collaboration among colleagues, as well as to disseminate results to decision-makers and the public. In addition, the web can be used to facilitate dynamic collaboration by allowing research-in-progress to be accessed and edited by multiple participants. Indeed, one key benefit of using the web to facilitate research projects is that data can undergo revisions and corrections while being displayed in some form over the web. However, it can be a challenge to keep track of multiple edits to files while maintaining the integrity of a web-interface. Here we describe a system for promoting collaboration in the creation, maintenance, and use of dynamic data over the web. Using simple Microsoft Excel spreadsheets combined with traditional relational databases to facilitate dynamic data generation and updates and maintain a data schema, we are able to facilitate efficient collaboration within and among participant communities. This approach allows for the separation of web display and content, which in turn allows participants to be responsible for their own content. This separation simplifies the interaction between those responsible for the content (researchers and other participants), those responsible for the data display (web designers), and those responsible for data management, storage, and retrieval (web programmers). The approach described here is particularly well suited to large, spatially-specific ecological datasets because it can be used to facilitate the “real-time” editing and display of visual data such as web-maps, as well as to encourage multiple participants to both contribute to and interact with “beta” data and content.     

Caries-preventive effect of glass ionomer and resin-based fissure sealants on permanent teeth: An update of systematic review evidence

Background

The 1980s marked the occasion when Geographical Information System (GIS) technology was broadly introduced into the geo-spatial community through the establishment of a strong GIS industry. This technology quickly disseminated across many countries, and has now become established as an important research, planning and commercial tool for a wider community that includes organisations in the public and private health sectors. The broad acceptance of GIS technology and the nature of its functionality have meant that numerous datasets have been created over the past three decades. Most of these datasets have been created independently, and without any structured documentation systems in place. However, search and retrieval systems can only work if there is a mechanism for datasets existence to be discovered and this is where proper metadata creation and management can greatly help. This situation must be addressed through support mechanisms such as Web-based portal technologies, metadata editor tools, automation, metadata standards and guidelines and collaborative efforts with relevant individuals and organisations. Engagement with data developers or administrators should also include a strategy of identifying the benefits associated with metadata creation and publication.

Findings

The establishment of numerous Spatial Data Infrastructures (SDIs), and other Internet resources, is a testament to the recognition of the importance of supporting good data management and sharing practices across the geographic information community. These resources extend to health informatics in support of research, public services and teaching and learning. This paper identifies many of these resources available to the UK academic health informatics community. It also reveals the reluctance of many spatial data creators across the wider UK academic community to use these resources to create and publish metadata, or deposit their data in repositories for sharing. The Go-Geo! service is introduced as an SDI developed to provide UK academia with the necessary resources to address the concerns surrounding metadata creation and data sharing. The Go-Geo! portal, Geodoc metadata editor tool, ShareGeo spatial data repository, and a range of other support resources, are described in detail.

Conclusions

This paper describes a variety of resources available for the health research and public health sector to use for managing and sharing their data. The Go-Geo! service is one resource which offers an SDI for the eclectic range of disciplines using GIS in UK academia, including health informatics. The benefits of data management and sharing are immense, and in these times of cost restraints, these resources can be seen as solutions to find cost savings which can be reinvested in more research.     

EpiExplorer: live exploration and global analysis of large epigenomic datasets

Epigenome mapping consortia are generating resources of tremendous value for studying epigenetic regulation. To maximize their utility and impact, new tools are needed that facilitate interactive analysis of epigenome datasets. Here we describe EpiExplorer, a web tool for exploring genome and epigenome data on a genomic scale. We demonstrate EpiExplorer's utility by describing a hypothesis-generating analysis of DNA hydroxymethylation in relation to public reference maps of the human epigenome. All EpiExplorer analyses are performed dynamically within seconds, using an efficient and versatile text indexing scheme that we introduce to bioinformatics. EpiExplorer is available at http://epiexplorer.mpi-inf.mpg.de.     

Mine, yours, ours? Sharing data on human genetic variation

The achievement of a robust, effective and responsible form of data sharing is currently regarded as a priority for biological and bio-medical research. Empirical evaluations of data sharing may be regarded as an indispensable first step in the identification of critical aspects and the development of strategies aimed at increasing availability of research data for the scientific community as a whole. Research concerning human genetic variation represents a potential forerunner in the establishment of widespread sharing of primary datasets. However, no specific analysis has been conducted to date in order to ascertain whether the sharing of primary datasets is common-practice in this research field. To this aim, we analyzed a total of 543 mitochondrial and Y chromosomal datasets reported in 508 papers indexed in the Pubmed database from 2008 to 2011. A substantial portion of datasets (21.9%) was found to have been withheld, while neither strong editorial policies nor high impact factor proved to be effective in increasing the sharing rate beyond the current figure of 80.5%. Disaggregating datasets for research fields, we could observe a substantially lower sharing in medical than evolutionary and forensic genetics, more evident for whole mtDNA sequences (15.0% vs 99.6%). The low rate of positive responses to e-mail requests sent to corresponding authors of withheld datasets (28.6%) suggests that sharing should be regarded as a prerequisite for final paper acceptance, while making authors deposit their results in open online databases which provide data quality control seems to provide the best-practice standard. Finally, we estimated that 29.8% to 32.9% of total resources are used to generate withheld datasets, implying that an important portion of research funding does not produce shared knowledge. By making the scientific community and the public aware of this important aspect, we may help popularize a more effective culture of data sharing.     

Responses of sympatric canids to human development revealed through citizen science

Measuring wildlife responses to anthropogenic activities often requires long‐term, large‐scale datasets that are difficult to collect. This is particularly true for rare or cryptic species, which includes many mammalian carnivores. Citizen science, in which members of the public participate in scientific work, can facilitate collection of large datasets while increasing public awareness of wildlife research and conservation. Hunters provide unique benefits for citizen science given their knowledge and interest in outdoor activities. We examined how anthropogenic changes to land cover impacted relative abundance of two sympatric canids, coyote (Canis latrans), and red fox (Vulpes vulpes) at a large spatial scale. In order to assess how land cover affected canids at this scale, we used citizen science data from bow hunter sighting logs collected throughout New York State, USA, during 2004–2017. We found that the two species had contrasting responses to development, with red foxes positively correlated and coyotes negatively correlated with the percentage of low‐density development. Red foxes also responded positively to agriculture, but less so when agricultural habitat was fragmented. Agriculture provides food and denning resources for red foxes, whereas coyotes may select forested areas for denning. Though coyotes and red foxes compete in areas of sympatry, we did not find a relationship between species abundance, likely a consequence of the coarse spatial resolution used. Red foxes may be able to coexist with coyotes by altering their diets and habitat use, or by maintaining territories in small areas between coyote territories. Our study shows the value of citizen science, and particularly hunters, in collection of long‐term data across large areas (i.e., the entire state of New York) that otherwise would unlikely be obtained.     

Sharing genomes: an integrated approach to funding, managing and distributing genomic clone resources

National biological resource centres have a vital role in archiving and distributing biological reagents that result from large-scale genome programmes. These reagents are invaluable to the research community as they enable independent validation of results disclosed in peer review and provide tools that facilitate the next steps in discovery science. Here we address the crucial issues of open access, quality of materials, integration with public databases and sustainability of resources.     

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.     

Ovarian Kaleidoscope database: ten years and beyond

Ovarian Kaleidoscope database (OKdb) is an online, searchable, public database containing text-based and DNA microarray data to facilitate research by ovarian researchers. Using key words and predetermined categories, users can search ovarian gene information based on gene function, cell type of expression, cellular localization, hormonal regulation, mutant phenotypes, chromosomal location, ligand-receptor relationship, and other criteria, either alone or in combination. For individual genes, users can access more than 10 extensive DNA microarray datasets to interrogate gene expression patterns in a development-specific and cell type-specific manner. All ligand and receptor genes expressed in the ovary are matched to facilitate investigation of paracrine/autocrine signaling. More than 3500 ovarian genes in the database are matched to 185 gene pathways in the Kyoto Encyclopedia of Genes and Genomes to allow for elucidation of gene interactions and relationships. In addition to >400 genes with infertility or subfertility phenotypes when mutated in mice or humans, the OKdb also lists ~50 and ~40 genes associated with polycystic ovarian syndrome and primary ovarian insufficiency, respectively. The expanding OKdb is updated weekly and allows submission of new genes by ovarian researchers to allow instant access to DNA microarray datasets for newly submitted genes. The present database is a virtual community for ovarian researchers and allows users to instantaneously provide their comments for individual gene pages based on an automated Web-discussion system. In the coming years, we will continue to add new features to serve the ovarian research community.     

ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome

Application of stem cell biology to breast cancer research has been limited by the lack of simple methods for identification and isolation of normal and malignant stem cells. Utilizing in vitro and in vivo experimental systems, we show that normal and cancer human mammary epithelial cells with increased aldehyde dehydrogenase activity (ALDH) have stem/progenitor properties. These cells contain the subpopulation of normal breast epithelium with the broadest lineage differentiation potential and greatest growth capacity in a xenotransplant model. In breast carcinomas, high ALDH activity identifies the tumorigenic cell fraction, capable of self-renewal and of generating tumors that recapitulate the heterogeneity of the parental tumor. In a series of 577 breast carcinomas, expression of ALDH1 detected by immunostaining correlated with poor prognosis. These findings offer an important new tool for the study of normal and malignant breast stem cells and facilitate the clinical application of stem cell concepts.     

JISTIC: Identification of Significant Targets in Cancer

Background  

Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect.     

How genomics can help biodiversity conservation

The availability of public genomic resources can greatly assist biodiversity assessment, conservation, and restoration efforts by providing evidence for scientifically informed management decisions. Here we survey the main approaches and applications in biodiversity and conservation genomics, considering practical factors, such as cost, time, prerequisite skills, and current shortcomings of applications. Most approaches perform best in combination with reference genomes from the target species or closely related species. We review case studies to illustrate how reference genomes can facilitate biodiversity research and conservation across the tree of life. We conclude that the time is ripe to view reference genomes as fundamental resources and to integrate their use as a best practice in conservation genomics.     

Identifying essential genes in Arabidopsis thaliana

Eight years after publication of the Arabidopsis genome sequence and two years before completing the first phase of an international effort to characterize the function of every Arabidopsis gene, plant biologists remain unable to provide a definitive answer to the following basic question: what is the minimal gene set required for normal growth and development? The purpose of this review is to summarize different strategies employed to identify essential genes in Arabidopsis, an important component of the minimal gene set in plants, to present an overview of the datasets and specific genes identified to date, and to discuss the prospects for future saturation of this important class of genes. The long-term goal of this collaborative effort is to facilitate basic research in plant biology and complement ongoing research with other model organisms.     

我国人口与健康状况简析

随着经济全球化格局的逐步形成,环境、生态、资源等与人类社会自身发展密切相关的因素已成为超越国界的共性课题。进入新世纪后,我国医药卫生事业面临着人口结构和疾病谱系迅速改变、公共卫生和医疗保健需求剧增、环境污染和食品安全保障等巨大挑战。在计划生育及生殖健康领域,在公共卫生与医疗网络领域,在医学研究与健康促进领域,以及在高新技术与健康产业领域,坚持科学的发展观,以最先进的科学技术研究成果为主导,推动关键技术的相互渗透及综合集成,提倡合理的生活方式已成为我国经济发展和社会进步的重要历史任务。     

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.     

Religion and the organizational context of immigrant civic engagement: Mexican Catholicism in the USA

This article complements emerging research on immigrant civic participation by examining how Catholic churches facilitate volunteer and political participation among first-generation Mexican immigrants. Drawing on interviews and ethnographic data, it identifies two reinforcing mechanisms that churches foster: small groups and organizational links. Specifically, small groups, like prayer groups, provide immigrants with intimate contexts for building networks, learning skills and sharing resources. On the other hand, churches cultivate links to secular organizations that sensitize immigrants to public debates and provide opportunities for civic participation. Put simply, church-going immigrants take advantage of broader civic opportunities because they gain resources and skills within small groups. Implications for understanding immigrant religion and civic participation more broadly are discussed.     

Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Background and objectiveBreast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.Materials and methodsThe digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.ResultsThe proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.