Evidence map›Paper›PMID 29764378›Full record

ArticleBMC genomics2018

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

Jeremie S Kim, Damla Senol Cali, Hongyi Xin, Donghyuk Lee, Saugata Ghose, Mohammed Alser, Hasan Hassan, Oguz Ergin, Can Alkan, Onur Mutlu

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. De Novo Assembly and Comparative Analysis of Mitochondrial Genomes of TwoInternational journal of molecular sciences · 2024
    Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jeremie S KimDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. jeremiekim123@gmail.com.
Damla Senol CaliDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Hongyi XinDepartment of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Donghyuk LeeNVIDIA Research, Austin, TX, USA.
Saugata GhoseDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Mohammed AlserDepartment of Computer Engineering, Bilkent University, Bilkent, Ankara, Turkey.
Hasan HassanDepartment of Computer Science, ETH Zürich, Zürich, CH, Switzerland.
Oguz ErginDepartment of Computer Engineering, TOBB University of Economics and Technology, Sogutozu, Ankara, Turkey.
Can AlkanDepartment of Computer Engineering, Bilkent University, Bilkent, Ankara, Turkey.
Onur MutluDepartment of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. omutlu@gmail.com.

Funding

Novel algorithms and hardware designs for ultra-fast next-gen sequence analysisR01HG006004 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI MUTLU, ONUR · 2011 to 2014
$1.4M
NHGRI NIH HHS R01 HG006004
6 · The paper itself

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.

Indexed as

AlgorithmsDatabases, GeneticGenome, HumanHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNASoftware3D-stacked DRAMEmerging memory technologiesGenome sequencingHigh throughput sequencingProcessing-in-memorySeed location filtering

Identifiers

PMID29764378
PMCPMC5954284

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.