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

FPGA Acceleration of Pre-Alignment Filters for Short Read Mapping With HLS

Pre-alignment filters are useful for reducing the computational requirements of genomic sequence mappers. Most of them are based on estimating or computing the edit distance between sequences and their candidate locations in a reference genome using a subset of the dynamic programming table used to compute Levenshtein distance. Some of their FPGA implementations of use classic HDL toolchains, thus limiting their portability. Currently, most FPGA accelerators offered by heterogeneous cloud providers support C/C++ HLS. In this work, we implement and optimize several state-of-the-art pre-alignment filters using C/C++ based-HLS to expand their portability to a wide range of systems supporting the OpenCL runtime. Moreover, we perform a complete analysis of the performance and accuracy of the filters and analyze the implications of the results. The maximum throughput obtained by an exact filter is 95.1 MPairs/s including memory transfers using 100 bp sequences, which is the highest ever reported for a comparable system and more than two times faster than previous HDL-based results. The best energy efficiency obtained from the accelerator (not considering host CPU) is 2.1 MPairs/J, more than one order of magnitude higher than other accelerator-based comparable approaches from the state of the art.10.13039/501100008530-European Union Regional Development Fund (ERDF) within the framework of the ERDF Operational Program of Catalonia 2014-2020 with a grant of 50% of the total cost eligible under the Designing RISC-V based Accelerators for next generation computers project (DRAC) (Grant Number: [001-P-001723]) 10.13039/501100002809-Catalan Government (Grant Number: 2017-SGR-313 and 2017-SGR-1624) 10.13039/501100004837-Spanish Ministry of Science, Innovation and Universities (Grant Number: PID2020-113614RB-C21 and RTI2018-095209-B-C22)Peer ReviewedPostprint (published version

FPGA acceleration of short read alignment with high-level synthesis

With the introduction of next-generation sequencing (NGS) technologies, DNA sequencing is becoming an increasingly widespread process. When performed on human patients, it can allow for the prediction and prevention of diseases. An essential part of this bioinformatics pipeline is short read alignment}, which refers to aligning short fragments of DNA to the large and expansive reference genome. This can be a very time-consuming process with much room for improvement. This thesis improves on Bowtie 2, an aligner that is already very popular and high-performing. Through the use of OpenCL, it is possible to parallelize this application for both GPU and FPGA by using the same code. Several different levels of parallelism are implemented in order to achieve speedup on Bowtie 2

Computational Strategies for Scalable Genomics Analysis.

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications

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