Accelerating Genome Analysis: A Primer on an Ongoing Journey
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
6 papers.
- 2022ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysiscited by 2
- 2022EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Acceleratorscited by 1
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databasescited by 1
- 2023ClaPIM: Scalable Sequence CLAssification using Processing-In-Memorycited by 0
- 2025AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chainingcited by 0
- 2026Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storagecited by 0
- 2026Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage15 Mar 2026
- 2025AGNES: Adaptive Graph Neural Network and Dynamic Programming Hybrid Framework for Real-Time Nanopore Seed Chaining15 Oct 2025
- 2023ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory16 Feb 2023
- 2022ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis20 Jul 2022
- 2022Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases29 May 2022
- 2022EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators4 Feb 2022