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Christopher Oehmen

Institution
Pacific Northwest National Laboratory

Projects

Bioinformatics Tools to Define the Proteomic State of the Cell

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
William Cannon
Project type
Capability Research
During the next three years we intend to enhance mass spectrometry-based proteomic analysis by building a new generation of tools for peptide identification. The accurate identification of protein…

High performance sequence analysis for data-intensive bioinformatics

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
Christopher Oehmen
Project type
Grand Challenge
Volume of sequence data available through public repositories are growing almost exponentially in time. Currently, the nonredundant protein database doubles in size approximately every 2 years. At…

Exploratory high-throughput sequence analysis on microbial genomes

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
Christopher Oehmen
Project type
Limited Scope
Microbial genomes are a fundamental source of sequence information vital to the DOE effort to find a viable strategy for remediating waste products accumulated by the atomic weapons program, and…

Scaling up for large metagenomic computations with ScalaBLAST

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
Christopher Oehmen
Project type
Capability Research
The recent emphasis on understanding genomes at the level of whole organisms from the U.S. Department of Energy, National Institutes of Health, and other major agencies has driven a worldwide effort…

Run ScalaBLAST on Proteomics Datasets

Lead Institution
Environmental Molecular Sciences Laboratory
Principal Investigator
Erich Vorpagel
This project is in support of the on going research within EMSL. As part of that support, MSCF computer time is being made available on an as-needed basis. The ultimate goal of high-throughput…

High-performance Support Vector Machines for Data-Intensive Applications

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
Christopher Oehmen
Project type
Limited Scope
Support vector machines (SVM) are a software technology with broad applicability for creating binary classifiers using training data. In much the same way that SCALAPACK efficiently enables many…

Optimization of peptide identification from tandem mass spectral data

Lead Institution
Pacific Northwest National Laboratory
Principal Investigator
William Cannon
Project type
Exploratory Research
Determining the correct sequence of amino acids for a peptide starting with MS/MS spectral data can be stated as an optimization problem where the objective is to match an experimental spectrum with…

Parallelizing Support Vector Machines

Lead Institution
Environmental Molecular Sciences Laboratory
Principal Investigator
Kevin Glass
The proposed project will focus on distributed and shared memory techniques to improve the execution efficiency and accuracy of Support Vector Machines (SVM). Current research suggests that SVM will…