Workshop on laboratory protocol standards for the Molecular Methods Database

A critical analysis of cancer biobank practices in relation to biospecimen quality

There are concerns that a substantial proportion of published research data is not reproducible, which may partially explain the frequent failure to translate pre-clinical results to clinical care. High-quality cancer biospecimens are needed for robust, reproducible research findings, with most researchers obtaining these specimens from cancer biobanks or tumour banks. This review provides an overview of the types of quality control (QC) activities conducted within cancer biobanks that pertain to biospecimen quality and of biospecimen quality reporting tools, including SPREC and BRISQ. We examine how QC assay results and other biospecimen data are communicated from biobanks to researchers, and whether these activities lead to improved biospecimen quality reporting within the literature and/or to improved research outcomes. We also discuss operational factors that limit QC activities within biobanks and evidence gaps requiring further research. In summary, whereas the provision of quality biospecimens is a common aim of cancer biobanks, QC activities remain underreported and are rarely discussed in the literature, compared with other aspects of biobank operations. Further research is required to determine how biobanks can most efficiently optimise biospecimen quality, and how communication between biobanks and researchers can be improved.

Toward a Literature-Driven Definition of Big Data in Healthcare

Objective. The aim of this study was to provide a definition of big data in healthcare. Methods. A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. Results. A total of 196 papers were included. Big data can be defined as datasets with Log⁡(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. Conclusion. Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.

Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC)

The first version of the Standard PREanalytical Code (SPREC) was developed in 2009 by the International Society for Biological and Environmental Repositories (ISBER) Biospecimen Science Working Group to facilitate documentation and communication of the most important preanalytical quality parameters of different types of biospecimens used for research. This same Working Group has now updated the SPREC to version 2.0, presented here, so that it contains more options to allow for recent technological developments. Existing elements have been fine tuned. An interface to the Biospecimen Reporting for Improved Study Quality (BRISQ) has been defined, and informatics solutions for SPREC implementation have been developed. A glossary with SPREC-related definitions has also been added.

EXACT2: the semantics of biomedical protocols

Background

The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility.

Methods

We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility.

To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols.

Results

The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed.

Conclusions

The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format.