The feasibility of using mobile health applications to conduct observational clinical studies requires rigorous validation. Here, we report initial findings from the Asthma Mobile Health Study, a research study, including recruitment, consent, and enrollment, conducted entirely remotely by smartphone. We achieved secure bidirectional data flow between investigators and 7,593 participants from across the United States, including many with severe asthma. Our platform enabled prospective collection of longitudinal, multidimensional data (e.g., surveys, devices, geolocation, and air quality) in a subset of users over the 6-month study period. Consistent trending and correlation of interrelated variables support the quality of data obtained via this method. We detected increased reporting of asthma symptoms in regions affected by heat, pollen, and wildfires. Potential challenges with this technology include selection bias, low retention rates, reporting bias, and data security. These issues require attention to realize the full potential of mobile platforms in research and patient care.

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from "data-to-knowledge-to-innovation," a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ ( geneanalytics.genecards.org ), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®--the human gene database; the MalaCards-the human diseases database; and the PathCards--the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®--the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene-tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell "cards" in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

Aims: The transcriptome and fate potential of three diverse human embryonic stem cell-derived clonal embryonic progenitor cell lines with markers of cephalic neural crest are compared when differentiated in the presence of combinations of TGFβ3, BMP4, SCF and HyStem-C matrices. Materials & methods: The cell lines E69 and T42 were compared with MEL2, using gene expression microarrays, immunocytochemistry and ELISA. Results: In the undifferentiated progenitor state, each line displayed unique markers of cranial neural crest including TFAP2A and CD24; however, none expressed distal HOX genes including HOXA2 or HOXB2, or the mesenchymal stem cell marker CD74. The lines also showed diverse responses when differentiated in the presence of exogenous BMP4, BMP4 and TGFβ3, SCF, and SCF and TGFβ3. The clones E69 and T42 showed a profound capacity for expression of endochondral ossification markers when differentiated in the presence of BMP4 and TGFβ3, choroid plexus markers in the presence of BMP4 alone, and leptomeningeal markers when differentiated in SCF without TGFβ3. Conclusion: The clones E69 and T42 may represent a scalable source of primitive cranial neural crest cells useful in the study of cranial embryology, and potentially cell-based therapy.

LifeMap Discovery™ provides investigators with an integrated database of embryonic development, stem cell biology and regenerative medicine. The hand-curated reconstruction of cell ontology with stem cell biology; including molecular, cellular, anatomical and disease-related information, provides efficient and easy-to-use, searchable research tools. The database collates in vivo and in vitro gene expression and guides translation from in vitro data to the clinical utility, and thus can be utilized as a powerful tool for research and discovery in stem cell biology, developmental biology, disease mechanisms and therapeutic discovery. LifeMap Discovery is freely available to academic nonprofit institutions at http://discovery.lifemapsc.com

A network of biological databases is reviewed, supplying a framework for studies of human genes and the association of their genomic variations with human phenotypes. The network is composed of GeneCards, the human gene compendium, which provides comprehensive information on all known and predicted human genes, along with its suite members GeneDecks and GeneLoc. Two databases are shown that address genes and variations focusing on olfactory reception (HORDE) and transduction (GOSdb). In the realm of disease scrutiny, we portray MalaCards, a novel comprehensive database of human diseases and their annotations. Also shown is GeneKid, a tool aimed at generating novel kidney disease biomarkers using systems biology, as well as Xome, a database for whole-exome next-generation DNA sequences for human diseases in the Israeli population. Finally, we show LifeMap Discovery, a database of embryonic development, stem cell research and regenerative medicine, which links to both GeneCards and MalaCards.

Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a precompetitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the "International Summit on Proteomics Data Release and Sharing Policy" in Amsterdam, The Netherlands, to identify and address potential roadblocks to rapid and open access to data. The six principles agreed upon by key stakeholders at the summit addressed issues surrounding (1) timing, (2) comprehensiveness, (3) format, (4) deposition to repositories, (5) quality metrics, and (6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest public repository for high-throughput gene expression data. Additionally, GEO hosts other categories of high-throughput functional genomic data, including those that examine genome copy number variations, chromatin structure, methylation status and transcription factor binding. These data are generated by the research community using high-throughput technologies like microarrays and, more recently, next-generation sequencing. The database has a flexible infrastructure that can capture fully annotated raw and processed data, enabling compliance with major community-derived scientific reporting standards such as ‘Minimum Information About a Microarray Experiment’ (MIAME). In addition to serving as a centralized data storage hub, GEO offers many tools and features that allow users to effectively explore, analyze and download expression data from both gene-centric and experiment-centric perspectives. This article summarizes the GEO repository structure, content and operating procedures, as well as recently introduced data mining features. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

What ID is, beyond what it is notTo the editor: Although your recent editorial 1 , "An intelligently designed response" was apposite, an important omission was apparent.Yes, debunking intelligent design (ID) by scientific reasoning requires good lay communication skills.Yes, merely (correctly) dismissing ID as nonsense will only fuel charges of scientific arrogance.And yes, the point about the nature of science has to be made because doing so makes palpable that ID is not science.However, the advice to avoid a religious discussion is questionableparticularly as so doing does not necessarily entail an atheistic rant.As well as emphasizing what ID is not, we also need to consider what it is.ID proponents eschew its association with literalist creationism but couple religious conservatism with a technology-friendly modernity.In the UK, we have a Christian organization, absurdly named 'Truth in Science' , which has distributed glossy paraphernalia to the science departments of secondary schools and sixth form colleges, advocating ID inclusion in science lessons.Despite contravening the national curriculum, this marketing ploy has apparently proven effective in persuading a number of schools that it has scientific credentials.ID appeals to fundamentalists of other religions.Harun Yahya, the pseudonymous vehicle for Muslim creationist propaganda, has distributed a lavish, 800-page tome to schools and universities, scientists and museums in France and the US.Thus, referring to religion is both unavoidable and necessary to understand the strategy at work here.ID is nothing more than sexed-up creationism for the media age, a realization necessary for an effective refutation of its scientific posturing.

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through the NCBI web site. NCBI resources include Entrez, the Entrez Programming Utilities, MyNCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Retroviral Genotyping Tools, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the web applications is custom implementation of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30 000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo .

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experimentcentric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www. ncbi.nlm.nih.gov/geo.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

The Minimum Information About a Microarray Experiment (MIAME) guidelines are a data content document developed by the Microarray Gene Expression Data (MGED) Society that outlines the information that should be provided when describing a microarray experiment1. Many journals and funding agencies have adopted the guidelines, with the aim of facilitating access to the elements of a study that would enable independent evaluation of results. However, the MIAME requirements have been criticized recently2, 3. The criticism stems, in part, from different interpretations of the level of detail required to adequately report a microarray experiment, and debates as to whether there is a genuine benefit to making microarray data public. The Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI)4 and ArrayExpress at the European Bioinformatics Institute (EBI)5 are the two major public databases of microarray data. Although they have different designs, both databases support capture of all data elements defined by MIAME. Figure 1 presents a timeline of major landmarks in the evolution of the GEO database, together with concomitant growth in submissions. GEO was launched in 2000, more than a year before the MIAME guidelines were proposed. Because there was not yet a consensus on reporting standards for microarray data, or even an obligation to make microarray data public, GEO initially allowed a minimal level of experimental detail to be supplied. Over the ensuing years we continually monitored the needs and requests of end-users, and gauged the level of effort submitters were realistically willing to invest in making their data public. We responded with incremental improvements to database design and curation standards, and we developed easy-to-generate batch deposit formats that significantly reduced the burden of submission and allowed contributors to focus on the content submitted rather than the mechanism of submission. Figure 1 Timeline of GEO growth and major landmarks in evolution of GEO database, and a screenshot of GEO tools which allow users to query, analyze, and visualize the data in GEO. In June 2005, we released major database revisions that included specific provisions for all MIAME data elements. In 2006, mechanisms for provision of raw data were further streamlined, and several MIAME elements that were previously optional became mandatory. Yet, even with these advances, it is still possible for a submitter to supply data that do not strictly adhere to the MIAME requirements. The difficulty lies in the fact that MIAME is a subjective set of guidelines where the level of detail to report is open to interpretation and, thus, cannot be unequivocally validated or enforced by computational means. All data submitted to GEO are syntactically validated for correct document structure, organization, and provision of basic elements. Next, each submission is inspected by curators for content integrity. GEO curators employ a pragmatic approach; we aim to ensure that sufficient information has been supplied to allow general interpretation of the experiment. Although encouraged, we have been less dogmatic with regards to provision of all-inclusive experimental protocols that would possibly permit practical replication of the entire experiment. Our reasoning is that provision of granulated experimental details adds a significant burden on the submitter, for (arguably) minimal real benefit for most end-users who are usually less concerned with this level of detail. When content or format problems are identified, curators work with the submitter until the issue is resolved. Submissions lacking critical descriptive elements necessary for overall experiment interpretation are not approved for public release. However, given the large diversity of biological themes, technologies, and statistical transformations applied to microarray data, it is impractical for curators to decisively determine the accuracy and validity of the data, or to assess if all relevant information has been supplied. This is where the role of reviewers and editors becomes important. The GEO database has had mechanisms for anonymous reviewer access to prepublication data since 2003. Over the last several years, authors have occasionally requested curator comment regarding the level of MIAME-compliance of their submissions, and we have been happy to offer feedback on areas that could be improved. GEO staff are similarly available to support reviewers and editors by providing tailored inspections of MIAME compliance of specific submissions upon request of the journal, as ArrayExpress is proposing to do6. If a reviewer determines that insufficient information has been supplied, the GEO database is designed such that authors can quickly respond by updating their records accordingly. It has been challenging to find the optimal balance between submitter effort and the appropriate level of metadata detail to request, all within a rapidly evolving technological and social environment7. However, the relative simplicity of the GEO database structure, together with common-sense curation policies that focus on gathering germane MIAME elements, have made it possible for us to develop an extensive suite of utilities that make the volumes of complex data archived at GEO accessible and easy to use by the research community at large8. Ultimately, the value of a database is reflected by how it is used by the community it serves. In the past month, GEO received approximately one million query hits, and over 200,000 file transfer downloads amounting to over 2.5 terabytes of compressed data. Furthermore, it is clear that researchers are applying these data to their own studies, as evidenced by over 100 recent publications citing data found in GEO to support or otherwise complement their own studies9. We view this as testament that the effort involved in making expression data public via GEO is fully justified.

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a Minimum Information About a Microarray Experiment-supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data at the level of individual genes or entire studies. This chapter describes how data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/.

Your Technology Feature on micro-array databases and standards, “Share and share alike” (Nature 442, 1069; 2006), is incorrect to state that the National Center for Biotechnology Information's Gene Expression Omnibus (GEO) database is not compliant with MIAME (minimum information about a microarray experiment) standards.GEO is in fact MIAME-compliant, as it fully supports capture of all data elements defined by MIAME and encourages use of the MIAME checklist in determining what information to submit about an experiment.