Quantitative evaluation of bias in PCR amplification and next-generation sequencing derived from metabarcoding samples.

Unbiased identification of organisms by PCR reactions using universal primers followed by DNA sequencing assumes positive amplification. We used six universal loci spanning 48 plant species and quantified the bias at each step of the identification process from end point PCR to next-generation sequencing. End point amplification was significantly different for single loci and between species. Quantitative PCR revealed that Cq threshold for various loci, even within a single DNA extraction, showed 2,000-fold differences in DNA quantity after amplification. Next-generation sequencing (NGS) experiments in nine species showed significant biases towards species and specific loci using adaptor-specific primers. NGS sequencing bias may be predicted to some extent by the Cq values of qPCR amplification.

Semantic integration of information about orthologs and diseases: the OGO system.

Semantic Web technologies like RDF and OWL are currently applied in life sciences to improve knowledge management by integrating disparate information. Many of the systems that perform such task, however, only offer a SPARQL query interface, which is difficult to use for life scientists. We present the OGO system, which consists of a knowledge base that integrates information of orthologous sequences and genetic diseases, providing an easy to use ontology-constrain driven query interface. Such interface allows the users to define SPARQL queries through a graphical process, therefore not requiring SPARQL expertise.

Automatically exposing OpenLifeData via SADI semantic Web Services.

BACKGROUND: Two distinct trends are emerging with respect to how data is shared, collected, and analyzed within the bioinformatics community. First, Linked Data, exposed as SPARQL endpoints, promises to make data easier to collect and integrate by moving towards the harmonization of data syntax, descriptive vocabularies, and identifiers, as well as providing a standardized mechanism for data access. Second, Web Services, often linked together into workflows, normalize data access and create transparent, reproducible scientific methodologies that can, in principle, be re-used and customized to suit new scientific questions. Constructing queries that traverse semantically-rich Linked Data requires substantial expertise, yet traditional RESTful or SOAP Web Services cannot adequately describe the content of a SPARQL endpoint. We propose that content-driven Semantic Web Services can enable facile discovery of Linked Data, independent of their location. RESULTS: We use a well-curated Linked Dataset - OpenLifeData - and utilize its descriptive metadata to automatically configure a series of more than 22,000 Semantic Web Services that expose all of its content via the SADI set of design principles. The OpenLifeData SADI services are discoverable via queries to the SHARE registry and easy to integrate into new or existing bioinformatics workflows and analytical pipelines. We demonstrate the utility of this system through comparison of Web Service-mediated data access with traditional SPARQL, and note that this approach not only simplifies data retrieval, but simultaneously provides protection against resource-intensive queries. CONCLUSIONS: We show, through a variety of different clients and examples of varying complexity, that data from the myriad OpenLifeData can be recovered without any need for prior-knowledge of the content or structure of the SPARQL endpoints. We also demonstrate that, via clients such as SHARE, the complexity of federated SPARQL queries is dramatically reduced.