MDI Biological Laboratory
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Lab awarded grant to advance cloud computing in Maine

MDIBL receives new grant to promote the shift to cloud computing among Maine INBRE undergraduates and researchers.

Benjamin L. King, Ph.D., teaches computational biology at the MDI Biological Laboratory. PHOTO COURTESY OF MDI BIOLOGICAL LABORATORY

The MDI Biological Laboratory has been awarded a grant to promote the shift to cloud computing among undergraduates and researchers at the 14 Maine educational and research institutions that comprise the federally funded Maine INBRE (IDeA Network of Biomedical Research Excellence) network, a program to strengthen biomedical research and research training in Maine.
The $126,449 grant is a one-year supplement to a five-year $18 million Maine INBRE grant awarded to the lab by the National Institute of General Medical Sciences (NIGMS) in 2019.  

The cloud refers to computational infrastructure and services that are located off-site and are owned by commercial providers such as Google. Cloud-based services include networks, servers, storage and applications and other tools and services for data computation and hosting that are accessible via the internet. Such services offer the advantages of being scalable, accessible, cost-effective, flexible and secure.  

The cloud lab program will address costs and time associated with analyzing the complex datasets being generated as a result of the advent of the genomic age. The volume and complexity of these datasets place a strain on researchers and institutions and inhibits the analysis and exchange of information among those at geographically distributed locations. 

Under the cloud lab program, Maine INBRE undergraduates will learn to use the Google Cloud Platform to develop workflows for the analysis of large datasets. The program will also provide research support to Maine INBRE scientists, graduate students and post-doctoral students, as well as assistance to Maine INBRE institutions in implementing and administering cloud computing services. 

Maine INBRE, which is led by the MDI Biological Laboratory, is one of four entities that will pilot the cloud lab program for NIGMS, an institute of the National Institutes of Health (NIH), with the others being the University of Arkansas, Historically Black Colleges and Universities and Tribal Colleges and Universities. 

In Maine, the INBRE network consists of 14 educational and research institutions, including University of Maine, The Jackson Laboratory and University of New England, as well as Bates, Bowdoin and Colby Colleges, College of the Atlantic, Southern Maine Community College, the University of Maine Honors College and the Universities of Maine at Farmington, Fort Kent, Machias and Presque Isle.  

The cloud lab program will be introduced in Maine INBRE courses for undergraduates from these institutions to be held at the MDI Biological Laboratory. 

The cloud lab program is part NIH’s STRIDES (Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability) initiative. 

Maine students will be trained in RNA sequencing analysis using a generic workflow analysis module developed by Benjamin L. King, Ph.D., assistant professor of bioinformatics at the University of Maine and co-director of the Maine INBRE  Bioinformatics Core. The module will use data on antibiotic-resistant non-tuberculosis mycobacteria generated by Sally D. Molloy, Ph.D., also of the University of Maine. 

The training module offers an example of how cloud computing can advance biomedical knowledge. Using the workflow, students can quickly identify genes associated with antibiotic resistance in non-tuberculosis mycobacteria. Such research could lead to the development of treatments that are effective against this type of infection, which can cause serious lung damage and is a growing health problem. 

The module allows students to execute analyses at a keystroke that otherwise would have taken days and would only have been possible at institutions with high performance computing capabilities, King said. Though the generic module can be adapted to other research, future plans call for teaching students and researchers how to develop analysis workflows that are customized to their research needs. 

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