GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies

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.

Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions

“…As we discuss in section 'Read mapping and polishing', further polishing may be required for improving the accuracy of the low-quality draft assemblies. For this purpose, after aligning the reads to the generated draft assembly with BWA-MEM or Minimap (We do not discuss these tools in great detail here, as they perform read mapping, which is commonly analyzed and relatively well understood [68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86]), one can use Nanopolish or Racon to perform polishing and obtain improved assemblies (i.e. consensus sequences).…”

Shouji: a fast and efficient pre-alignment filter for sequence alignment

“…SHD (Xin et al, 2015) is a SIMD-friendly bit-vector filter that provides higher filtering accuracy compared to the Adjacency Filter. GRIM-Filter (Kim et al, 2018) exploits the high memory bandwidth and the logic layer of 3D-stacked memory to perform highly-parallel filtering in the DRAM chip itself. GateKeeper (Alser et al, 2017a) is designed to utilize the large amounts of parallelism offered by FPGA architectures.…”

Enabling Practical Processing in and near Memory for Data-Intensive Computing

“…Ambit: In-DRAM Bulk Bitwise Operations. Many applications use bulk bitwise operations [51,99] (i.e., bitwise operations on large bit vectors), such as bitmap indices, bitwise scan acceleration [62] for databases, accelerated document filtering for web search [25], DNA sequence alignment [6,7,47,100], encryption algorithms [28,98], graph processing, and networking [99]. Accelerating bulk bitwise operations can thus significantly boost the performance and energy efficiency of a wide range of applications.…”