GateKeeper: A New Hardware Architecture for Accelerating Pre-Alignment in DNA Short Read Mapping
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Abstract (arXiv)
Motivation: High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -- called short reads -- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and "candidate" locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (1) it is implemented using quadratic-time dynamic programming algorithms, and (2) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment operations. Results: We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. GateKeeper can be integrated with any mapper that performs sequence alignment for verification. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing up to 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10. Availability: https://github.com/BilkentCompGen/GateKeeper
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2016.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2022ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysiscited by 2
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- 2022LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learningcited by 1
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learningcited by 1
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning26 Mar 2025
- 2022LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning22 Aug 2022
- 2022ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis20 Jul 2022
- 2022EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators4 Feb 2022