Application of a bioinformatics training delivery method for reaching dispersed and distant trainees.

Many initiatives have addressed the global need to upskill biologists in bioinformatics tools and techniques. Australia is not unique in its requirement for such training, but due to its large size and relatively small and geographically dispersed population, Australia faces specific challenges. A combined training approach was implemented by the authors to overcome these challenges. The "hybrid" method combines guidance from experienced trainers with the benefits of both webinar-style delivery and concurrent face-to-face hands-on practical exercises in classrooms. Since 2017, the hybrid method has been used to conduct 9 hands-on bioinformatics training sessions at international scale in which over 800 researchers have been trained in diverse topics on a range of software platforms. The method has become a key tool to ensure scalable and more equitable delivery of short-course bioinformatics training across Australia and can be easily adapted to other locations, topics, or settings.

The proteome browser web portal.

In 2010, the Human Proteome Organization launched the Human Proteome Project (HPP), aimed at identifying and characterizing the proteome of the human body. To support complete coverage, one arm of the project will take a gene- or chromosomal-centric strategy (C-HPP) aimed at identifying at least one protein product from each protein-coding gene. Despite multiple large international biological databases housing genomic and protein data, there is currently no single system that integrates updated pertinent information from each of these data repositories and assembles the information into a searchable format suitable for the type of global proteomics effort proposed by the C-HPP. We have undertaken the goal of producing a data integration and analysis software system and browser for the C-HPP effort and of making data collections from this resource discoverable through metadata repositories, such as Australian National Data Service's Research Data Australia. Here we present our vision and progress toward the goal of developing a comprehensive data integration and analysis software tool that provides a snapshot of currently available proteomic related knowledge around each gene product, which will ultimately assist in analyzing biological function and the study of human physiology in health and disease.

EMAGE mouse embryo spatial gene expression database: 2010 update.

EMAGE (http://www.emouseatlas.org/emage) is a freely available online database of in situ gene expression patterns in the developing mouse embryo. Gene expression domains from raw images are extracted and integrated spatially into a set of standard 3D virtual mouse embryos at different stages of development, which allows data interrogation by spatial methods. An anatomy ontology is also used to describe sites of expression, which allows data to be queried using text-based methods. Here, we describe recent enhancements to EMAGE including: the release of a completely re-designed website, which offers integration of many different search functions in HTML web pages, improved user feedback and the ability to find similar expression patterns at the click of a button; back-end refactoring from an object oriented to relational architecture, allowing associated SQL access; and the provision of further access by standard formatted URLs and a Java API. We have also increased data coverage by sourcing from a greater selection of journals and developed automated methods for spatial data annotation that are being applied to spatially incorporate the genome-wide (approximately 19,000 gene) 'EURExpress' dataset into EMAGE.

EMAGE--Edinburgh Mouse Atlas of Gene Expression: 2008 update.

EMAGE (http://genex.hgu.mrc.ac.uk/Emage/database) is a database of in situ gene expression patterns in the developing mouse embryo. Domains of expression from raw data images are spatially integrated into a set of standard 3D virtual mouse embryos at different stages of development, allowing data interrogation by spatial methods. Sites of expression are also described using an anatomy ontology and data can be queried using text-based methods. Here we describe recent enhancements to EMAGE which include advances in spatial search methods including: a refined local spatial similarity search algorithm, a method to allow global spatial comparison of patterns in EMAGE and subsequent hierarchical-clustering, and spatial searches across multiple stages of development. In addition, we have extended data access by the introduction of web services and new HTML-based search interfaces, which allow access to data that has not yet been spatially annotated. We have also started incorporating full 3D images of gene expression that have been generated using optical projection tomography (OPT).

EMAGE: a spatial database of gene expression patterns during mouse embryo development.

EMAGE (http://genex.hgu.mrc.ac.uk/Emage/database) is a freely available, curated database of gene expression patterns generated by in situ techniques in the developing mouse embryo. It is unique in that it contains standardized spatial representations of the sites of gene expression for each gene, denoted against a set of virtual reference embryo models. As such, the data can be interrogated in a novel and abstract manner by using space to define a query. Accompanying the spatial representations of gene expression patterns are text descriptions of the sites of expression, which also allows searching of the data by more conventional text-based methods.

Best practice data life cycle approaches for the life sciences.

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.

Biobank classification in an Australian setting.

In 2011, Watson and Barnes proposed a schema for classifying biobanks into 3 groups (mono-, oligo-, and poly-user), primarily based upon biospecimen access policies. We used results from a recent comprehensive survey of cancer biobanks in New South Wales, Australia to assess the applicability of this biobank classification schema in an Australian setting. Cancer biobanks were identified using publically available data, and by consulting with research managers. A comprehensive survey was developed and administered through a face-to-face setting. Data were analyzed using Microsoft Excel™ 2010 and IBM SPSS Statistics™ version 21.0. The cancer biobank cohort (n=23) represented 5 mono-user biobanks, 7 oligo-user biobanks, and 11 poly-user biobanks, and was analyzed as two groups (mono-/oligo- versus poly-user biobanks). Poly-user biobanks employed significantly more full-time equivalent staff, and were significantly more likely to have a website, share staff between biobanks, access governance support, utilize quality control measures, be aware of biobanking best practice documents, and offer staff training. Mono-/oligo-user biobanks were significantly more likely to seek advice from other biobanks. Our results further delineate a biobank classification system that is primarily based on access policy, and demonstrate its relevance in an Australian setting.

EMAGE: Electronic Mouse Atlas of Gene Expression.

The EMAGE (Electronic Mouse Atlas of Gene Expression) database (http://www.emouseatlas.org/emage) allows users to perform on-line queries of mouse developmental gene expression. EMAGE data are represented spatially using a framework of 3D mouse embryo models, thus allowing uniquely spatial queries to be carried out alongside more traditional text-based queries. This spatial representation of the data also allows a comparison of spatial similarity between the expression patterns. The data are mapped to the models by a team of curators using bespoke mapping software, and the associated meta-data are curated for accuracy and completeness. The data contained in EMAGE are gathered from three main sources: from the published literature, through large-scale screens and collaborations, and via direct submissions from researchers. There are a variety of ways to query the EMAGE database via the on-line search interfaces, as well as via direct computational script-based queries. EMAGE is a free, on-line, community resource funded by the Medical Research Council, UK.

Minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE).

One purpose of the biomedical literature is to report results in sufficient detail that the methods of data collection and analysis can be independently replicated and verified. Here we present reporting guidelines for gene expression localization experiments: the minimum information specification for in situ hybridization and immunohistochemistry experiments (MISFISHIE). MISFISHIE is modeled after the Minimum Information About a Microarray Experiment (MIAME) specification for microarray experiments. Both guidelines define what information should be reported without dictating a format for encoding that information. MISFISHIE describes six types of information to be provided for each experiment: experimental design, biomaterials and treatments, reporters, staining, imaging data and image characterizations. This specification has benefited the consortium within which it was developed and is expected to benefit the wider research community. We welcome feedback from the scientific community to help improve our proposal.