Robert Boissy, Ph.D. specializes in responsible bioinformatics and has provided a more comprehensive overview of what his genomics and bioinformatics research and teaching interests are, and advice he can respectfully offer domain-specific research colleagues, under the general theme of responsible bioinformatics: 

What? (research area)

Why? (benefit)

Statistically sound research study design

Statistical power analysis and bio-analytical assay design are essential research topics that investigators should review with UNMC's CCORDA statistics group. 

Research metadata management (RMM)

Intra-site and inter-site RMM for project, people and sample provenance is fundamental to achieving the goal of reproducible bioinformatic research, especially for large collaborations.

Scientific workflow management

To help make bioinformatics analysis procedures modular, comprehensible, reproducible, reusable, shareable, and teachable.

The development and use of NGS research reference standards and NGS reference standard materials

Bioinformatic analyses of sparse high-dimensional NGS data benefit immensely from the availability of reference standards and reference standard DNA materials such as those provided by the NIST and others.  These resources provide traceable, community accepted gold standards for analyses such as DNA sequence variation detection.

Hybrid cloud computing - the combined use of on-premises and public compute clouds for scientific research.

1) On-demand efficient use of computing hardware for research and teaching.

2) Joint analyses of local confidential research data sets (on-premises) and relevant massive public research data sets (in public compute clouds).

3) Fulfillment of a funding agency's requirement for data sharing as part of a funded grant's Data Management Plan (DMP).

Socially responsible computing using the most appropriate, energy-efficient and cost-effective mix of heterogeneous microprocessors: central processing units (CPUs); general-purpose graphics processing units (GPGPUs); and field-programmable gate arrays (FPGAs).

Ubiquitous NGS will be a reality soon.  Several of the fundamental steps in well-established NGS data analysis algorithms can be executed much faster and with much greater energy efficiency in reconfigurable hardware circuits than in software running on general-purpose CPUs.  The time, energy and cost efficiency savings that reconfigurable hardware compute accelerators like FPGAs can achieve matter because: (a) in the future tens of millions of compute-intensive personal genome NGS data analyses will be carried out annually in the U.S. alone; and (b) sometimes, the time-to-result of an NGS-based biomedical analysis may be a matter of life or death.

NGS and the humanities

Using NGS data to study historical patterns of the flow of human haplotype blocks can help inform our understanding of demographic and personal genealogical history, and in some cases help individuals recover the loss of their legal rights.

Outreach to educators in rural school districts in Nebraska

New resources like public compute clouds and the maker movement open up exciting opportunities for rural grade-school students in Nebraska to cultivate their sense of wonder, curiosity and initiative about scientific discovery.

Collaborations with industry

Academics can learn a lot from industry, and vice-versa.  I welcome and actively pursue opportunities to collaborate with industry partners and sponsors, especially in the computing industry.

 

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