The enormous growth of biological sequence data has caused bioinformatics to be rapidly moving towards a data-intensive, computational science. As a result, the computational power needed by bioinformatics applications is growing rapidly as well. The recent emergence of parallel accelerator technologies such as GPUs has made it possible to significantly reduce the execution times of many bioinformatics applications. In this talk I will present the design and implementation of scalable GPU algorithms based on the CUDA programming model in order to accelerate important bioinformatics applications. In particular, I will focus on algorithms and tools for next-generation sequencing (NGS) using error correction as an example.Detection and correction of sequencing errors is an important but time-consuming pre-processing step for de-novo genome assembly or read mapping. In this talk, I discuss the parallel algorithm design used for the CUDA-EC and DecGPU tools. I will also give an overview of other CUDA-enabled tools developed by my research group.