Evidence map›Paper›PMID 28575161›Full record

ArticleBioinformatics (Oxford, England)2017

GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping.

Mohammed Alser, Hasan Hassan, Hongyi Xin, Oguz Ergin, Onur Mutlu, Can Alkan

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
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  3. WFA-GPU: gap-affine pairwise read-alignment using GPUs.Bioinformatics (Oxford, England) · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Mohammed AlserDepartment of Computer Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey.
Hasan HassanTOBB University of Economics & Technology, Sogutozu, Ankara, Turkey.
Hongyi XinDepartment of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Oguz ErginTOBB University of Economics & Technology, Sogutozu, Ankara, Turkey.
Onur MutluDepartment of Computer Science, ETH Zürich, 8092 Zürich, Switzerland.
Can AlkanDepartment of Computer Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationHigh 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: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) 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 algorithms.

resultsWe 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. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 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 AND IMPLEMENTATION: https://github.com/BilkentCompGen/GateKeeper. CONTACT: mohammedalser@bilkent.edu.tr or onur.mutlu@inf.ethz.ch or calkan@cs.bilkent.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

SoftwareAlgorithmsGenome, HumanHigh-Throughput Nucleotide SequencingHumansSequence AlignmentSequence Analysis, DNA

Identifiers

PMID28575161
PMCPMC5860160

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.