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

Michael Olejnik, Michel Steuwer, Sergei Gorlatch, Dominik Heider

Research output: Contribution to journalArticlepeer-review

Abstract

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.deContact: d.heider@wz-straubing.de
Original languageEnglish
Pages (from-to)3272-3273
Number of pages2
JournalBioinformatics
Volume30
Issue number22
DOIs
Publication statusPublished - 15 Nov 2014

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