Intelligent Computing, Networking, and Informatics pp 1001-1006 | Cite as
GenSeeK: A Novel Parallel Multiple Pattern Recognition Algorithm for DNA Sequences
DNA sequences are huge in size, and the genome databases are growing exponentially every year. One of the key elements in computational biology is genomic data. There are many real-time applications, such as DNA profiling and real-time crime investigation, which requires the biological subjects DNA sequences at real time. To retrieve this, data in real time require lot of computational power and resources. Throughput is one of the main bottleneck for applications such as DNA sequence searching or pattern matching. This paper presents a new DNA sequence multiple pattern recognition algorithm which computes on compressed space. This algorithm is efficient in terms of computational complexity and the amount of resources required during the computation in real time, the main reason for this behavior is that it does the computations on compressed sequences. This algorithm is implemented using index-based technique, and the sequential code is optimized. The proposed algorithm is mainly focused on achieving good comparison per character ratio as well as high throughput. The parallel version of the algorithm is implemented using multicore for achieving high throughput. The techniques used in development of this algorithm can be directly translated into huge DNA database search.
KeywordsPattern matching Multicore
We would like to dedicate this work to founder Chancellor of SSSIHL, Bhagawan Sri Sathya Sai Baba. Without His grace, this work would have remained a dream for us. This work was partially supported by a NVIDIA grant under professor partnership program and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science foundation grant number OCI-1053575.
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