5,279 research outputs found

    A highly parameterized and efficient FPGA-based skeleton for pairwise biological sequence alignment

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    Rust-Bio - a fast and safe bioinformatics library

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    We present Rust-Bio, the first general purpose bioinformatics library for the innovative Rust programming language. Rust-Bio leverages the unique combination of speed, memory safety and high-level syntax offered by Rust to provide a fast and safe set of bioinformatics algorithms and data structures with a focus on sequence analysis

    Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

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    The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics

    Thread-cooperative, bit-parallel computation of Levenshtein distance on GPU

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    Approximate string matching is a very important problem in computational biology; it requires the fast computation of string distance as one of its essential components. Myers' bit-parallel algorithm improves the classical dynamic programming approach to Levenshtein distance computation, and offers competitive performance on CPUs. The main challenge when designing an efficient GPU implementation is to expose enough SIMD parallelism while at the same time keeping a relatively small working set for each thread. In this work we implement and optimise a CUDA version of Myers' algorithm suitable to be used as a building block for DNA sequence alignment. We achieve high efficiency by means of a cooperative parallelisation strategy for (1) very-long integer addition and shift operations, and (2) several simultaneous pattern matching tasks. In addition, we explore the performance impact obtained when using features specific to the Kepler architecture. Our results show an overall performance of the order of tera cells updates per second using a single high-end Nvidia GPU, and factor speedups in excess of 20 with respect to a sixteen-core, non-vectorised CPU implementation
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