Abstract
Computational detection of TF binding patterns has become an indispensable tool in functional genomics research. With the rapid advance of new sequencing technologies, large amounts of protein-DNA interaction data have been produced. Analyzing this data can provide substantial insight into the mechanisms of transcriptional regulation. However, the massive amount of sequence data presents daunting challenges. In our previous work, we have developed a novel algorithm called Hybrid Motif Sampler (HMS) that enables more scalable and accurate motif analysis. Despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position. Using the NVIDIA CUDA toolkit, we developed a graphics processing unit (GPU)-accelerated motif analysis program named GPUmotif. We proposed a “fragmentation" technique to hide data transfer time between memories. Performance comparison studies showed that commonly-used model-based motif scan and de novo motif finding procedures such as HMS can be dramatically accelerated when running GPUmotif on NVIDIA graphics cards. As a result, energy consumption can also be greatly reduced when running motif analysis using GPUmotif. The GPUmotif program is freely available at http://sourceforge.net/projects/gpumotif/
Highlights
Locating the transcription factor (TF)-DNA interaction sites provides insight into the underlying mechanisms of transcriptional regulation
We discussed the limitations of current methods and proposed a new de novo motif finding algorithm named Hybrid Motif Sampler (HMS) [6]
We have developed a software package named GPUmotif that is capable of performing ultra-fast motif analysis
Summary
Locating the transcription factor (TF)-DNA interaction sites provides insight into the underlying mechanisms of transcriptional regulation. Since binding sites for most TFs show sequence specificity, computational prediction of TF binding sites based on such sequence features has demonstrated to be an effective tool for functional genomics research. New technologies such as ChIP-Seq, or chromatin immunoprecipitation followed by high-throughput sequencing [1,2,3,4], are capable of producing large amounts of sequence data that is believed to harbor protein-DNA binding sites. Motif analyses including known motif scan and de novo motif finding are effective tools to help us understand the underlying transcription regulation mechanisms [5]. Because HMS is a probability model-based method, which relies on parameter-rich position-specific weight matrices (PSWM) to characterize motif patterns, despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position for every sequence through an iterative process
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