Susie Stephens

Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html .

Over the years, the Bio-Ontologies SIG at ISMB has provided a forum for discussion of the latest and most innovative research in the application of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. The ten papers selected for this supplement are extended versions of the original papers presented at the 2010 SIG. The papers span a wide range of topics including practical solutions for data and knowledge integration for translational medicine, hypothesis based querying , understanding kidney and urinary pathways, mining the pharmacogenomics literature; theoretical research into the orthogonality of biomedical ontologies, the representation of diseases, the representation of research hypotheses, the combination of ontologies and natural language processing for an annotation framework, the generation of textual definitions, and the discovery of gene interaction networks.

There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, e-Neuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data. Results: We have constructed a pilot ontology for BrainPharm (a subset of SenseLab) using RDFS and then converted a subset of the BrainPharm data into RDF according to the ontological structure. We have also integrated the converted BrainPharm data with existing RDF hypothesis and publication data from a pilot version of SWAN (Semantic Web Applications in Neuromedicine). Our implementation uses the RDF Data Model in Oracle Database 10g release 2 for data integration, query, and inference, while our Web interface allows users to query the data and retrieve the results in a convenient fashion. Conclusion: Accessing and integrating biomedical data which cuts across multiple disciplines will be increasingly indispensable and beneficial to neuroscience researchers. The Semantic Web approach we undertook has demonstrated a promising way to semantically integrate data sets created independently. It also shows how advanced queries and inferences can be performed over the integrated data, which are hard to achieve using traditional data integration approaches. Our pilot results suggest that our Semantic Web approach is suitable for realizing e-Neuroscience and generic enough to be applied in other biomedical fields.

Background: A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthenTranslational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.Results: We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.Conclusion: Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.