FPGA acceleration of reference-based compression for genomic data

One of the key challenges facing genomics today is efficiently storing the massive amounts of data generated by next-generation sequencing platforms. Reference-based compression is a popular strategy for reducing the size of genomic data, whereby sequence information is encoded as a mapping to a known reference sequence. Determining the mapping is a computationally intensive problem, and is the bottleneck of most reference-based compression tools currently available. This paper presents the first FPGA acceleration of reference-based compression for genomic data. We develop a new mapping algorithm based on the FM-index search operation which includes optimisations targeting the compression ratio and speed. Our hardware design is implemented on a Maxeler MPC-X2000 node comprising 8 Altera Stratix V FPGAs. When evaluated against compression tools currently available, our tool achieves a superior compression ratio, compression time, and energy consumption for both FASTA and FASTQ formats. For example, our tool achieves a 30% higher compression ratio and is 71.9 times faster than the fastqz tool

FPGA Acceleration of Reference-Based Compression for Genomic Data

Abstract-One of the key challenges facing genomics today is efficiently storing the massive amounts of data generated by nextgeneration sequencing platforms. Reference-based compression is a popular strategy for reducing the size of genomic data, whereby sequence information is encoded as a mapping to a known reference sequence. Determining the mapping is a computationally intensive problem, and is the bottleneck of most referencebased compression tools currently available. This paper presents the first FPGA acceleration of reference-based compression for genomic data. We develop a new mapping algorithm based on the FM-index search operation which includes optimisations targeting the compression ratio and speed. Our hardware design is implemented on a Maxeler MPC-X2000 node comprising 8 Altera Stratix V FPGAs. When evaluated against compression tools currently available, our tool achieves a superior compression ratio, compression time, and energy consumption for both FASTA and FASTQ formats. For example, our tool achieves a 30% higher compression ratio and is 71.9 times faster than the fastqz tool

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

Parallel approach to sliding window sums

Sliding window sums are widely used in bioinformatics applications, including sequence assembly, k-mer generation, hashing and compression. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost for the bioinformatics applications. We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). When applied to the genomic application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5X.Comment: 10 pages, 5 figure

RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes

Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either 1) require powerful computational resources that may not be available for portable sequencers or 2) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: 1) read mapping, 2) relative abundance estimation, and 3) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8x and 3.4x better average throughput and 2) an average speedup of 32.1x and 2.1x in the mapping time, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash