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.
Highlights
Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor
We find that a bin window of 4096 bins provides enough parallelism to completely hide the filtering latency while the read mapper running on the CPU performs sequence alignment
GRIM-Filter is orthogonal to these works, and can Discussion We have shown that GRIM-Filter significantly reduces the execution time of read mappers by reducing the number of unnecessary sequence alignments and by taking advantage of processing-in-memory using 3D-stacked DRAM technology
Summary
Seed 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. Seed-and-extend mappers [1,2,3,4,5,6] are a class of read mappers that break down each read sequence into seeds (i.e., smaller segments) to find locations in the reference genome that closely match the read. The mapper indexes a data structure with each seed to obtain a list of possible locations within the reference genome that could result in a match (❸). The mapper aligns the read sequence to the reference sequence (❺), using an expensive sequence alignment (i.e., verification) algorithm to determine the similarity between the read sequence and the reference sequence
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