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In the last decades there is an exponential growth in the amount of genomic data that need to be analyzed. A very important problem in biology is the extraction of the biologically functional genomic DNA from the actual genome of the organisms. There have been proposed many computational biology algorithms that solve the gene finding problem which utilize various approaches; GlimmerHMM is considered...
The massively parallel computing using graphical processing unit (GPU), which based on tens of thousands of parallel threats within hundreds of GPU's streaming processors, has gained broad popularity and attracted researchers in a wide range of application areas from finance, computer aided engineering, computational fluid dynamics, game physics, numerics, science, medical imaging, life science, and...
Sequence alignment algorithms such as the Smith-Waterman algorithm are among the most important applications in the development of bioinformatics. Sequence alignment algorithms must process large amounts of data which may take a long time. Here, we introduce our Adaptive Hybrid Multiprocessor technique to accelerate the implementation of the Smith-Waterman algorithm. Our technique utilizes both the...
Next-generation, high-throughput sequencers are now capable of producing hundreds of billions of short sequences (reads) in a single day. The task of accurately mapping the reads back to a reference genome is of particular importance because it is used in several other biological applications, e.g., genome re-sequencing, DNA methylation, and ChiP sequencing. On a personal computer (PC), the computationally...
Local Sequence alignment is an important task for bioinformatics. The most widely used algorithm is Smith-Waterman has a quadratic time complexity which is time consuming especially in large biological database search. Many attempts were made to accelerate Smith-Waterman using parallel architecture. In this paper a parallel implementation of Smith Waterman algorithm will be presented. This implementation...
GPUs offer drastically different performance characteristics compared to traditional multicore architectures. To explore the tradeoffs exposed by this difference, we refactor MUMmer, a widely-used, highly-engineered bioinformatics application which has both CPU- and GPU-based implementations. We synthesize our experience as three high-level guidelines to design efficient GPU-based applications. First,...
Sequence alignment is one of the most fundamental and important operation in Bioinformatics. Among lots of Sequence alignment tools, Blast is one of the most popular algorithms. In this paper, we describe the primary strategy of a GPU-based parallel computing on Blast program.
Markov clustering is becoming a key algorithm with in bioinformatics for determining clusters in networks. For instance, clustering protein interaction networks is helping find genes implicated in diseases such as cancer. However, with fast sequencing and other technologies generating vast amounts of data on biological networks, performance and scalability issues are becoming a critical limiting factorin...
In this paper we present an implementation of the Smith-Waterman algorithm. The implementation is done in OpenCL and targets high-end GPUs. This implementation is capable of computing similarity indexes between reference and query sequences. The implementation is designed for the sequence alignment paths calculation. In addition, it is capable of handling very long reference sequences (in the order...
This paper introduces the implementation of Koza-style tree-based Genetic Programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza's books and then on a real world problem inspired from aeronautics, that extends...
Computer games that handle realistic environments are becoming more popular in the game market. Games that make use of natural environments such as the spreading of fire or the flow of water need to be very carefully designed. In order to produce a desired effect of fire or water, a designer needs to try and test map properties several times. There has been an effort to use genetic algorithm to find...
DNA analysis is an emerging application of high performance bioinformatics. Modern sequencing machinery are able to provide, in few hours, large input streams of data which needs to be matched against exponentially growing databases of known fragments. The ability to recognize these patterns effectively and fastly may allow extending the scale and the reach of the investigations performed by biology...
The graphics processing unit (GPU) has evolved from being a fixed-function processor with programmable stages into a programmable processor with many fixed-function components that deliver massive parallelism. By modifying the GPU's stream processor to support “general-purpose computation” on the GPU (GPGPU), applications that perform massive vector operations can realize many orders-of-magnitude...
Motif is overrepresented pattern in biological sequence and motif finding is an important problem in bioinformatics. Due to high computational complexity of motif finding, more and more computational capabilities are required as the rapid growth of available biological data, such as gene transcription data. Among many motif finding algorithms, Gibbs sampling is an effective method for long motif finding...
In bioinformatics, alignments are commonly performed in genome and protein sequence analysis for gene identification and evolutionary similarities. There are several approaches for such analysis, each varying in accuracy and computational complexity. Smith-Waterman (SW) is by far the best algorithm for its accuracy in similarity scoring. However, execution time of this algorithm on general purpose...
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