Research Software Portal
Provided by ACCESS
Data Discovery
Quantifying and Improving the Accuracy of Detecting Genomic Variation
Description: This project will improve the robustness of error analysis and correction by developing new root-cause error analysis methods and hybrid error-correction algorithms accelerated via FPGA or GPU implementations. Variant calling will be improved by both suppressing errors and building new machine-learning based techniques that would work with clinical workflows.
Data Resources Type: Data
Attributes
| Quality Level | Production |
| Audience | uiuc.edu |
| Topics | data,research_computing |
| Provided By | Center for Computational Biotechnology and Genomic Medicine (CCBGM) |
© ACCESS All Rights Reserved.
ACCESS is an advanced computing and data resource supported by the
National Science Foundation
and made possible through these lead institutions and their partners:
Carnegie Mellon University,
University of Colorado Boulder,
University of Illinois at Urbana-Champaign, and
State University of New York at Buffalo .
Administrative Login