gCUP

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.

gCUP: rapid GPU-based HIV-1 co-receptor usage prediction for next-generation sequencing.

gCUP is a software tool to predict and classify HIV-1 coreceptor usage  Next-generation sequencing (NGS). The gCUP algorithm uses a novel model for HIV tropism and runs on NVIDIA GeForce GTX GPUs.

gCUP

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