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Instructional Support

The Picotte cluster may be used for instruction, on approval from the URCF Board. Educational accounts are subject to additional terms of use. To use Picotte in your coursework, please contact URCF Support at urcf-support@drexel.edu .

Past Courses

The following past courses have utilized the Picotte Cluster.

BIO 331/631 & ECES490/640 (Fall 2014) Bioinformatics - Instructor: Gail Rosen

This course focuses on developing the computational, algorithmic, and database navigational skills required to analyze genomic data.  First, an intensive bash and Biopython review is conducted.  Then, we illustrate statistical signal processing concepts such as dynamic programming, hidden markov models, information theoretic measures, and assessing statistical significance.  The goals are achieved through lecture and computer exercises that focus on querying genomic databases, genome annotation via hidden markov models, sequence alignment through dynamic programming, and phylogenetics with maximum likelihood approaches.  

ECES 490/690 (Spring 2015) Statistical Analysis of Genomics - Instructor: Gail Rosen

This course focuses on development of the computational and database navigational skills required to analyze metagenomic data that have become available with the development of high throughput genomic technologies.  Students will learn Python and R to analysis genomics.  Many third party packages to analyze genomics such and assess the statistical significance of results will be learned.  The goals will be achieved through lecture and lab exercises that focus on genomic databases, programming for importing and pre-processing genomic data, high performance programming for analysis of high-throughput metagenomic analyses, and use of high-performance computing for microbiome composition, comparison, and functional analyses.  

MATH 540 (Spring 2015) Numerical Computing - Instructor: Gideon Simpson

Math 540 will introduce students to a variety of mature C language libraries for scientific computing with the goal of pushing them beyond Matlab. Problems from linear algebra (Ax = b), numerical partial differential equations (−∆u = f, ut = ∆u), and Monte Carlo methods (computing π), will be explored using MPI, PETSc, and GSL. Particular emphasis will be placed on abstracting mathematical problems and coding them into distinct modules. Additional topics will include parallel computing on distributed architectures with PETSc and automated finite elements with FEniCS While the course will emphasize development in C, students will also be instructed on how to interface compiled code with Matlab and Python for rapid testing and flexibility.