ArticleBMC genomics2018
GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A survey of sequence-to-graph mapping algorithms in the pangenome era.Genome biology · 2025Review
- FPGA-based accelerator for adaptive banded event alignment in nanopore sequencing data analysis.BMC bioinformatics · 2025Article
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- De Novo Assembly and Comparative Analysis of Mitochondrial Genomes of TwoInternational journal of molecular sciences · 2024Article
- A framework for high-throughput sequence alignment using real processing-in-memory systems.Bioinformatics (Oxford, England) · 2023Article
- BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis.NAR genomics and bioinformatics · 2023Article
- From molecules to genomic variations: Accelerating genome analysis via intelligent algorithms and architectures.Computational and structural biotechnology journal · 2022Review
- Technology dictates algorithms: recent developments in read alignment.Genome biology · 2021Review
- Hardware acceleration of genomics data analysis: challenges and opportunities.Bioinformatics (Oxford, England) · 2021Article
- Accel-Align: a fast sequence mapper and aligner based on the seed-embed-extend method.BMC bioinformatics · 2021Article
- Shouji: a fast and efficient pre-alignment filter for sequence alignment.Bioinformatics (Oxford, England) · 2019Article
- Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions.Briefings in bioinformatics · 2019Review
- GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies.BMC genomics · 2018Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundSeed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments.
resultsWe propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm.
conclusionGRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.
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