Research Methods in Life Sciences Commons^™

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

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 …

Phylogenetic Search Through Partial Tree Mixing., Kenneth Sundberg, Mark Clement, Quinn Snell, Dan Ventura, Michael Whiting, Keith Crandall

Computational Biology Institute

BACKGROUND: Recent advances in sequencing technology have created large data sets upon which phylogenetic inference can be performed. Current research is limited by the prohibitive time necessary to perform tree search on a reasonable number of individuals. This research develops new phylogenetic algorithms that can operate on tens of thousands of species in a reasonable amount of time through several innovative search techniques.

RESULTS: When compared to popular phylogenetic search algorithms, better trees are found much more quickly for large data sets. These algorithms are incorporated in the PSODA application available at http://dna.cs.byu.edu/psoda

CONCLUSIONS: The use of Partial Tree Mixing …

A Genomic Island In Salmonella Enterica Ssp. Salamae Provides New Insights On The Genealogy Of The Locus Of Enterocyte Effacement., P Scott Chandry, Simon Gladman, Sean C Moore, Torsten Seemann, Keith A Crandall, Narelle Fegan

Computational Biology Institute

The genomic island encoding the locus of enterocyte effacement (LEE) is an important virulence factor of the human pathogenic Escherichia coli. LEE typically encodes a type III secretion system (T3SS) and secreted effectors capable of forming attaching and effacing lesions. Although prominent in the pathogenic E. coli such as serotype O157:H7, LEE has also been detected in Citrobacter rodentium, E. albertii, and although not confirmed, it is likely to also be in Shigella boydii. Previous phylogenetic analysis of LEE indicated the genomic island was evolving through stepwise acquisition of various components. This study describes a new LEE region from two …