Shouji: A Fast and Efficient Pre-Alignment Filter for Sequence Alignment

Motivation: The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly-parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern FPGA (Field-Programmable Gate Array) architectures to further boost the performance of our algorithm. Results: Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8x. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step. Availability: https://github.com/CMU-SAFARI/ShoujiComment: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz234/5421509, Bioinformatics Journal 201

MAGNET: Understanding and Improving the Accuracy of Genome Pre-Alignment Filtering

In the era of high throughput DNA sequencing (HTS) technologies, calculating the edit distance (i.e., the minimum number of substitutions, insertions, and deletions between a pair of sequences) for billions of genomic sequences is the computational bottleneck in todays read mappers. The shifted Hamming distance (SHD) algorithm proposes a fast filtering strategy that can rapidly filter out invalid mappings that have more edits than allowed. However, SHD shows high inaccuracy in its filtering by admitting invalid mappings to be marked as correct ones. This wastes the execution time and imposes a large computational burden. In this work, we comprehensively investigate four sources that lead to the filtering inaccuracy. We propose MAGNET, a new filtering strategy that maintains high accuracy across different edit distance thresholds and data sets. It significantly improves the accuracy of pre-alignment filtering by one to two orders of magnitude. The MATLAB implementations of MAGNET and SHD are open source and available at: https://github.com/BilkentCompGen/MAGNET.Comment: 10 Pages, 13 Figure

DESIGN AND CHARACTERIZATION OF LOW-POWER LOW-NOISE ALLDIGITAL SERIAL LINK FOR POINT-TO-POINT COMMUNICATION IN SOC

The fully-digital implementation of serial links has recently emerged as a viable alternative to their classical analogue counterpart. Indeed, reducing the analogue content in favour of expanding the digital content becomes more attractive due to the ability to achieve less power consumption, less sensitivity to the noise and better scalability across multiple technologies and platforms with inconsiderable modifications. In addition, describing the circuit in hardware description languages gives it a high flexibility to program all design parameters in a very short time compared with the analogue designs which need to be re-designed at transistor level for any parameter change. This can radically reduce cost and time-to-market by saving a significant amount of development time. However, beside these considerable advantages, the fully-digital architecture poses several design challenges

SneakySnake: A Fast and Accurate Universal Genome Pre-Alignment Filter for CPUs, GPUs, and FPGAs

Motivation: We introduce SneakySnake, a highly parallel and highly accurate pre-alignment filter that remarkably reduces the need for computationally costly sequence alignment. The key idea of SneakySnake is to reduce the approximate string matching (ASM) problem to the single net routing (SNR) problem in VLSI chip layout. In the SNR problem, we are interested in finding the optimal path that connects two terminals with the least routing cost on a special grid layout that contains obstacles. The SneakySnake algorithm quickly solves the SNR problem and uses the found optimal path to decide whether or not performing sequence alignment is necessary. Reducing the ASM problem into SNR also makes SneakySnake efficient to implement on CPUs, GPUs, and FPGAs. Results: SneakySnake significantly improves the accuracy of pre-alignment filtering by up to four orders of magnitude compared to the state-of-the-art pre-alignment filters, Shouji, GateKeeper, and SHD. For short sequences, SneakySnake accelerates Edlib (state-of-the-art implementation of Myers's bit-vector algorithm) and Parasail (state-of-the-art sequence aligner with a configurable scoring function), by up to 37.7x and 43.9x (>12x on average), respectively, with its CPU implementation, and by up to 413x and 689x (>400x on average), respectively, with FPGA and GPU acceleration. For long sequences, the CPU implementation of SneakySnake accelerates Parasail and KSW2 (sequence aligner of minimap2) by up to 979x (276.9x on average) and 91.7x (31.7x on average), respectively. As SneakySnake does not replace sequence alignment, users can still obtain all capabilities (e.g., configurable scoring functions) of the aligner of their choice, unlike existing acceleration efforts that sacrifice some aligner capabilities. Availability: https://github.com/CMU-SAFARI/SneakySnakeComment: To appear in Bioinformatic

Accelerating Genome Analysis: A Primer on an Ongoing Journey

Genome analysis fundamentally starts with a process known as read mapping, where sequenced fragments of an organism's genome are compared against a reference genome. Read mapping is currently a major bottleneck in the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are able to sequence a genome much faster than the computational techniques employed to analyze the genome. We describe the ongoing journey in significantly improving the performance of read mapping. We explain state-of-the-art algorithmic methods and hardware-based acceleration approaches. Algorithmic approaches exploit the structure of the genome as well as the structure of the underlying hardware. Hardware-based acceleration approaches exploit specialized microarchitectures or various execution paradigms (e.g., processing inside or near memory). We conclude with the challenges of adopting these hardware-accelerated read mappers.Comment: This is an extended and updated version of a paper published in IEEE Micro, vol. 40, no. 5, pp. 65-75, 1 Sept.-Oct. 2020, https://doi.org/10.1109/MM.2020.301372