Introduction

SUMMARY: Next-generation sequencing (NGS) has a large potential in HIV diagnostics, and genotypic prediction models have been developed and successfully tested in the recent years. However, albeit being highly accurate, these computational models lack computational efficiency to reach their full potential. In this study, we demonstrate the use of graphics processing units (GPUs) in combination with a computational prediction model for HIV tropism. Our new model named gCUP, parallelized and optimized for GPU, is highly accurate and can classify >175 000 sequences per second on an NVIDIA GeForce GTX 460. The computational efficiency of our new model is the next step to enable NGS technologies to reach clinical significance in HIV diagnostics. Moreover, our approach is not limited to HIV tropism prediction, but can also be easily adapted to other settings, e.g. drug resistance prediction. AVAILABILITY AND IMPLEMENTATION: The source code can be downloaded at http://www.heiderlab.de CONTACT: d.heider@wz-straubing.de.

Publications

  1. gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.
    Cite this
    Olejnik M, Steuwer M, Gorlatch S, Heider D, 2014-11-01 - Bioinformatics (Oxford, England)

Credits

  1. Michael Olejnik
    Developer

    Institute of Computer Science, University of Muenster, Germany

  2. Michel Steuwer
    Developer

    Institute of Computer Science, University of Muenster, Germany

  3. Sergei Gorlatch
    Developer

    Institute of Computer Science, University of Muenster, Germany

  4. Dominik Heider
    Investigator

    Institute of Computer Science, University of Muenster, Germany

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT001061
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Country/RegionGermany
Submitted ByDominik Heider