133 research outputs found
Dataflow acceleration of Smith-Waterman with Traceback for high throughput Next Generation Sequencing
Smith-Waterman algorithm is widely adopted bymost popular DNA sequence aligners. The inherent algorithmcomputational intensity and the vast amount of NGS input datait operates on, create a bottleneck in genomic analysis flows forshort-read alignment. FPGA architectures have been extensivelyleveraged to alleviate the problem, each one adopting a differentapproach. In existing solutions, effective co-design of the NGSshort-read alignment still remains an open issue, mainly due tonarrow view on real integration aspects, such as system widecommunication and accelerator call overheads. In this paper, wepropose a dataflow architecture for Smith-Waterman Matrix-filland Traceback alignment stages, to perform short-read alignmenton NGS data. The architectural decision of moving both stages onchip extinguishes the communication overhead, and coupled withradical software restructuring, allows for efficient integration intowidely-used Bowtie2 aligner. This approach delivers×18 speedupover the respective Bowtie2 standalone components, while our co-designed Bowtie2 demonstrates a 35% boost in performance
Genomic co-processor for long read assembly
Genomics data is transforming medicine and our understanding of life in fundamental ways; however, it is far outpacing Moore's Law. Third-generation sequencing technologies produce 100X longer reads than second generation technologies and reveal a much broader mutation spectrum of disease and evolution. However, these technologies incur prohibitively high computational costs. In order to enable the vast potential of exponentially growing genomics data, domain specific acceleration provides one of the few remaining approaches to continue to scale compute performance and efficiency, since general-purpose architectures are struggling to handle the huge amount of data needed for genome alignment. The aim of this project is to implement a genomic-coprocessor targeting HPC FPGAs starting from the Darwin FPGA co-processor. In this scenario, the final objective is the simulation and implementation of the algorithms described by Darwin using Alveo boards, exploiting High Bandwidth Memory (HBM) to increase its performance
SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications
Summary: The Smith Waterman (SW) algorithm, which produces the optimal
pairwise alignment between two sequences, is frequently used as a key component
of fast heuristic read mapping and variation detection tools, but current
implementations are either designed as monolithic protein database searching
tools or are embedded into other tools. To facilitate easy integration of the
fast Single Instruction Multiple Data (SIMD) SW algorithm into third party
software, we wrote a C/C++ library, which extends Farrars Striped SW (SSW) to
return alignment information in addition to the optimal SW score. Availability:
SSW is available both as a C/C++ software library, as well as a stand alone
alignment tool wrapping the librarys functionality at
https://github.com/mengyao/Complete- Striped-Smith-Waterman-Library Contact:
[email protected]: 3 pages, 2 figure
Accelerated large-scale multiple sequence alignment
<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p
Comparative Analysis of Computationally Accelerated NGS Alignment
The Smith-Waterman algorithm is the basis of most current sequence alignment technology, which can be used to identify similarities between sequences for cancer detection and treatment because it provides researchers with potential targets for early diagnosis and personalized treatment. The growing number of DNA and RNA sequences available to analyze necessitates faster alignment processes than are possible with current iterations of the Smith-Waterman (S-W) algorithm. This project aimed to identify the most effective and efficient methods for accelerating the S-W algorithm by investigating recent advances in sequence alignment. Out of a total of 22 articles considered in this project, 17 articles had to be excluded from the study due to lack of standardization of data reporting. Only one study by Chen et al. obtained in this project contained enough information to compare accuracy and alignment speed. When accuracy was excluded from the criteria, five studies contained enough information to rank their efficiency. The study conducted by Rucci et al. was the fastest at 268.83 Giga Cell Updates Per Second (GCUPS), and the method by Pérez-Serrano et al. came close at 229.93 GCUPS while testing larger sequences. It was determined that reporting standards in this field are not sufficient, and the study by Chen et al. should set a benchmark for future reporting
State-of-the-art in Smith-Waterman Protein Database Search on HPC Platforms
Searching biological sequence database is a common and repeated task in bioinformatics and molecular biology. The Smith–Waterman algorithm is the most accurate method for this kind of search. Unfortunately, this algorithm is computationally demanding and the situation gets worse due to the exponential growth of biological data in the last years. For that reason, the scientific community has made great efforts to accelerate Smith–Waterman biological database searches in a wide variety of hardware platforms. We give a survey of the state-of-the-art in Smith–Waterman protein database search, focusing on four hardware architectures: central processing units, graphics processing units, field programmable gate arrays and Xeon Phi coprocessors. After briefly describing each hardware platform, we analyse temporal evolution, contributions, limitations and experimental work and the results of each implementation. Additionally, as energy efficiency is becoming more important every day, we also survey performance/power consumption works. Finally, we give our view on the future of Smith–Waterman protein searches considering next generations of hardware architectures and its upcoming technologies.Instituto de Investigación en InformáticaUniversidad Complutense de Madri
DOPA: GPU-based protein alignment using database and memory access optimizations
Background Smith-Waterman (S-W) algorithm is an optimal sequence alignment method for biological databases, but its computational complexity makes it too slow for practical purposes. Heuristics based approximate methods like FASTA and BLAST provide faster solutions but at the cost of reduced accuracy. Also, the expanding volume and varying lengths of sequences necessitate performance efficient restructuring of these databases. Thus to come up with an accurate and fast solution, it is highly desired to speed up the S-W algorithm. Findings This paper presents a high performance protein sequence alignment implementation for Graphics Processing Units (GPUs). The new implementation improves performance by optimizing the database organization and reducing the number of memory accesses to eliminate bandwidth bottlenecks. The implementation is called Database Optimized Protein Alignment (DOPA) and it achieves a performance of 21.4 Giga Cell Updates Per Second (GCUPS), which is 1.13 times better than the fastest GPU implementation to date. Conclusions In the new GPU-based implementation for protein sequence alignment (DOPA), the database is organized in equal length sequence sets. This equally distributes the workload among all the threads on the GPU's multiprocessors. The result is an improved performance which is better than the fastest available GPU implementation.MicroelectronicsElectrical Engineering, Mathematics and Computer Scienc
Large-Scale Pairwise Sequence Alignments on a Large-Scale GPU Cluster
This paper presents design of a GPU kernel for performing pairwise sequence alignments for large-scale short sequence datasets generated by nextgeneration sequencers. This kernel principally performs batch Needleman– Wunsch global alignments. When used with its MPI-based host software, the kernel is scalable and is capable of achieving high throughput alignment when run on a CPU-GPU cluster
- …