The Ark: a customizable web-based data management tool for health and medical research.

Summary: The Ark is an open-source web-based tool that allows researchers to manage health and medical research data for humans and animals without specialized database skills or programming expertise. The system provides data management for core research information including demographic, phenotype, biospecimen and pedigree data, in addition to supporting typical investigator requirements such as tracking participant consent and correspondence, whilst also being able to generate custom data exports and reports. The Ark is 'study generic' by design and highly configurable via its web interface, allowing researchers to tailor the system to the specific data management requirements of their study. Availability and Implementation: Source code for The Ark can be obtained freely from the website https://github.com/The-Ark-Informatics/ark/ . The source code can be modified and redistributed under the terms of the GNU GPL v3 license. Documentation and a pre-configured virtual appliance can be found at the website http://sphinx.org.au/the-ark/ . Contact: adrianb@unimelb.edu.au. Supplementary information: Supplementary data are available at Bioinformatics online.

A Need for Better Understanding Is the Major Determinant for Public Perceptions of Human Gene Editing.

The CRISPR/Cas system could provide an efficient and reliable means of editing the human genome and has the potential to revolutionize modern medicine; however, rapid developments are raising complex ethical issues. There has been significant scientific debate regarding the acceptability of some applications of CRISPR/Cas, with leaders in the field highlighting the need for the lay public's views to shape expert discussion. As such, we sought to determine the factors that influence public opinion on gene editing. We created a 17-item online survey translated into 11 languages and advertised worldwide. Topic modeling was used to analyze textual responses to determine what factors influenced respondents' opinions toward human somatic or embryonic gene editing, and how this varied among respondents with differing attitudes and demographic backgrounds. A total of 3,988 free-text responses were analyzed. Respondents had a mean age of 32 (range, 11-90) years, and 37% were female. The most prevalent topics cited were Future Generations, Research, Human Editing, Children, and Health. Respondents who disagreed with gene editing for health-related purposes were more likely to cite the topic Better Understanding than those who agreed to both somatic and embryonic gene editing. Respondents from Western backgrounds more frequently discussed Future Generations, compared with participants from Eastern countries. Religious respondents did not cite the topic Religious Beliefs more frequently than did nonreligious respondents, whereas Christian respondents were more likely to cite the topic Future Generations. Our results suggest that public resistance to human somatic or embryonic gene editing does not stem from an inherent mistrust of genome modification, but rather a desire for greater understanding. Furthermore, we demonstrate that factors influencing public opinion vary greatly amongst demographic groups. It is crucial that the determinants of public attitudes toward CRISPR/Cas be well understood so that the technology does not suffer the negative public sentiment seen with previous genetic biotechnologies.

A Global Social Media Survey of Attitudes to Human Genome Editing.

Ongoing breakthroughs with CRISPR/Cas-based editing could potentially revolutionize modern medicine, but there are many questions to resolve about the ethical implications for its therapeutic application. We conducted a worldwide online survey of over 12,000 people recruited via social media to gauge attitudes toward this technology and discuss our findings here.

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.