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Research Methods in Life Sciences Commons™
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Full-Text Articles in Research Methods in Life Sciences
A Quick Guide For Building A Successful Bioinformatics Community., Aidan Budd, Manuel Corpas, Michelle D Brazas, Jonathan C Fuller, Jeremy Goecks, Nicola J Mulder, Magali Michaut, B F Francis Ouellette, Aleksandra Pawlik, Niklas Blomberg
A Quick Guide For Building A Successful Bioinformatics Community., Aidan Budd, Manuel Corpas, Michelle D Brazas, Jonathan C Fuller, Jeremy Goecks, Nicola J Mulder, Magali Michaut, B F Francis Ouellette, Aleksandra Pawlik, Niklas Blomberg
Computational Biology Institute
"Scientific community" refers to a group of people collaborating together on scientific-research-related activities who also share common goals, interests, and values. Such communities play a key role in many bioinformatics activities. Communities may be linked to a specific location or institute, or involve people working at many different institutions and locations. Education and training is typically an important component of these communities, providing a valuable context in which to develop skills and expertise, while also strengthening links and relationships within the community. Scientific communities facilitate: (i) the exchange and development of ideas and expertise; (ii) career development; (iii) coordinated funding …
Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson
Pathoscope: Species Identification And Strain Attribution With Unassembled Sequencing Data., Owen E Francis, Matthew Bendall, Solaiappan Manimaran, Changjin Hong, Nathan L Clement, Eduardo Castro-Nallar, Quinn Snell, G Bruce Schaalje, Mark J Clement, Keith A Crandall, W Evan Johnson
Computational Biology Institute
Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence …