A key mission of the Center for Computation and Visualization is to support research activities that depend on advanced computing and visualization techniques. Below is a list of research projects that have made extensive use of CCV resources.

Please contact us if you are a CCV user and would like to feature your research on this page.

Dunn Lab completes evolutionary tree for mollusks

mollusca: Credit: Casey Dunn/Brown UniversityMontage of mollusks photographed in Belize. Credit: Casey Dunn/Brown University

October, 2011
[See Brown's press release for more details.]

In a paper in Nature, researchers from the Casey Dunn Lab (in the Department of Ecology and Evolutionary Biology) and collaborating institutions have compiled the most comprehensive evolutionary tree for mollusks to date. Their analysis surprisingly places two enigmatic groups, cephalopods and monoplacophorans, as sister clades. The team has also shown that there was a single origin for shelled mollusks.

To accomplish this, the team sequenced thousands of genes and matched them up through intensive computational analyses using the Center for Computation and Visualization's flagship computing cluster Oscar. Stephen Smith, a postdoctoral researcher in the Dunn Lab, designed the computational analysis pipeline, which combines the teams collected data with publicly available data from the NCBI dbEST database using a modified version of the PartiGene tool. Next, the pipeline assembles the raw reads using Velvet/Oases (Illumina data) and Newbler/CAP3 (Roche 454 data). Assemblies are compared against a subset (Fungi/Metazoa) of the NCBI nr database with BLASTX, using the scalable mpiBLAST package available on Oscar, and the resulting hits are translated with prot4EST. Pairwise comparisons from an all-to-all BLASTP are used to cluster genes into homologues using the MCL algorithm. Finally, phylogenetic analyses using RAxML, MrBayes, and PhyloBayes divide the homologues into orthologues. Full details of the analysis are available as a supplement to the Nature paper.

Prior to sequencing, the team collected hard-to-find specimens through a global sampling effort, including a group of organisms thought until recently to be extinct for millions of years. In all, the team collected specimens for 15 species.

Evolutionary Gene Networks unravel the genetic mechanisms that enable parasites to thrive

Lane gene networks: Credit: Ian Misner/URIVisualizations of several gene networks, showing differences in connectedness and topology. Credit: Ian Misner/URI

March, 2011

The Lane Lab (Department of Biological Sciences, University of Rhode Island) focuses on genome evolution and reduction in parasites. Parasitic organisms have independently evolved in every major lineage of life on Earth, but despite both the medical, economic and agricultural impacts of parasites, very little is known about the process by which a species adopts a parasitic life style.

By combining their own data with publicly available genomes, the Lane Lab has amassed a comparative dataset of nearly 30 genomes to investigate changes specific to a parasitic lifestyle, but these analyses are computationally intensive. In an effort to streamline massive comparative genomic analysis, the Lane Lab utilizes Evolutionary Gene Networks (EGNs) to compute pair-wise comparisons on a genome scale. EGNs offer a powerful visual and analysis tool to understanding genome evolution. Yet, a typical run of the EGN pipeline took up to 8 days on the Lane Lab's Mac Pro workstation. With help from the CCV User Services Group, this runtime was reduced to less than a day by scaling up the computationally-intensive all-to-all BLASTN component of the pipeline using the mpiBLAST package on Oscar.

See past research projects.