1,645 research outputs found
Reconfigurable acceleration of genetic sequence alignment: A survey of two decades of efforts
Genetic sequence alignment has always been a computational challenge in bioinformatics. Depending on the problem size, software-based aligners can take multiple CPU-days to process the sequence data, creating a bottleneck point in bioinformatic analysis flow. Reconfigurable accelerator can achieve high performance for such computation by providing massive parallelism, but at the expense of programming flexibility and thus has not been commensurately used by practitioners. Therefore, this paper aims to provide a thorough survey of the proposed accelerators by giving a qualitative categorization based on their algorithms and speedup. A comprehensive comparison between work is also presented so as to guide selection for biologist, and to provide insight on future research direction for FPGA scientists
FPGA acceleration of DNA sequence alignment: design analysis and optimization
Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows.
1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy.
2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software.
3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces
SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
Sequence alignment forms an important backbone in many sequencing
applications. A commonly used strategy for sequence alignment is an approximate
string matching with a two-dimensional dynamic programming approach. Although
some prior work has been conducted on GPU acceleration of a sequence alignment,
we identify several shortcomings that limit exploiting the full computational
capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated
sequence alignment library focused on seed extension. Based on the analysis of
previous work with real-world sequencing data, we propose techniques to exploit
the data locality and improve workload balancing. The experimental results
reveal that SaLoBa significantly improves the seed extension kernel compared to
state-of-the-art GPU-based methods.Comment: Published at IPDPS'2
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
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