Jessica D. Tenenbaum

BioWarehouse: a bioinformatics database warehouse toolkit

BackgroundThis article addresses the problem of interoperation of heterogeneous bioinformatics databases.ResultsWe introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research.ConclusionBioWarehouse embodies significant progress on the database integration problem for bioinformatics.

The coming age of data-driven medicine: translational bioinformatics' next frontier

Last year, in 2011, we argued that biomedical informatics stands ready to revolutionize human health and healthcare using large-scale measurements on a large number of individuals.1 We anticipated that, with the coming changes in the amount and diversity of datasets, data-centric approaches that compute on massive amounts of data (often called ‘Big Data’2 ,3) to discover patterns and to make clinically relevant predictions would be increasingly common in translational bioinformatics. Given these trends, we programmed the 2012 Summit on Translational Bioinformatics to focus on research that takes us from base pairs to the bedside,4 with a particular emphasis on clinical implications of mining massive datasets, and bridging the latest multimodal measurement technologies with the large amounts of electronic healthcare data that are increasingly available. The coming year did turn out to be the year of Big Data for the Summit, with multiple submissions on managing and interpreting large datasets (figure 1). Among the 35 full paper submissions to the Summit, four stood out for their innovation, and hence the authors were invited to expand the work for this special issue of JAMIA —adding to the growing presence of translational bioinformatics in the journal.5–9 Figure 1 A tag cloud generated from the title and abstracts of the submissions made to the AMIA Translational Bioinformatics Summit 2012. The more frequently used the words are, the larger they appear. ‘Data’ was the most commonly mentioned word across all submissions for 2012. Liu et al 10 demonstrated how the ability to predict adverse drug reactions can be increased by integrating chemical, biological, and phenotypic properties of drugs. They demonstrated that prediction accuracy increased from 0.9054 (when only chemical structures were used) to 0.9524 (when chemical structures along with biological and phenotypic features were used). They conclude that data fusion … Correspondence to Dr Nigam H Shah, Stanford University School of Medicine, 1265 Welch Road, Room X-229, Stanford, CA 94305, USA; nigam{at}stanford.edu

The Biomedical Resource Ontology (BRO) to enable resource discovery in clinical and translational research

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.

Evaluation of microarray surfaces and arraying parameters for autoantibody profiling.

Autoantigen microarrays are being used increasingly to study autoimmunity. Significant variation has been observed when comparing microarray surfaces, printing methods, and probing conditions. In the present study, 24 surfaces and several arraying parameters were analyzed using >500 feature autoantigen microarrays printed with quill pins. A small subset of slides, including FAST®, PATH®, and SuperEpoxy2, performed well while maintaining the sensitivity and specificity of autoantigen microarrays previously demonstrated by our laboratory. By optimizing the major variables in our autoantigen microarray platform, subtle differences in serum samples can be identified that will shed light on disease pathogenesis.

A sea of standards for omics data: sink or swim?

In the era of Big Data, omic-scale technologies, and increasing calls for data sharing, it is generally agreed that the use of community-developed, open data standards is critical. Far less agreed upon is exactly which data standards should be used, the criteria by which one should choose a standard, or even what constitutes a data standard. It is impossible simply to choose a domain and have it naturally follow which data standards should be used in all cases. The ‘right’ standards to use is often dependent on the use case scenarios for a given project. Potential downstream applications for the data, however, may not always be apparent at the time the data are generated. Similarly, technology evolves, adding further complexity. Would-be standards adopters must strike a balance between planning for the future and minimizing the burden of compliance. Better tools and resources are required to help guide this balancing act.

Metabolic network failures in Alzheimer's disease—A biochemical road map

The Alzheimers Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimers disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.

An informatics research agenda to support precision medicine: seven key areas

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM’s vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

Translational Bioinformatics: Past, Present, and Future

Though a relatively young discipline, translational bioinformatics (TBI) has become a key component of biomedical research in the era of precision medicine. Development of high-throughput technologies and electronic health records has caused a paradigm shift in both healthcare and biomedical research. Novel tools and methods are required to convert increasingly voluminous datasets into information and actionable knowledge. This review provides a definition and contextualization of the term TBI, describes the discipline’s brief history and past accomplishments, as well as current foci, and concludes with predictions of future directions in the field.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort

Alzheimer’s disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association

The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research.