Evidence map›Paper›PMID 31955674›Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2020

The parallelism motifs of genomic data analysis.

Katherine Yelick, Aydın Buluç, Muaaz Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake, Marquita Ellis, Evangelos Georganas, Giulia Guidi and 4 more

Abstract read
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In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

14 authors.

Katherine YelickLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Aydın BuluçLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Muaaz AwanLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Ariful AzadSchool of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
Benjamin BrockLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Rob EganDOE Joint Genome Institute, Walnut Creek, CA, USA.
Saliya EkanayakeLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Marquita EllisLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Evangelos GeorganasIntel Labs, Santa Clara, CA, USA.
Giulia GuidiLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Steven HofmeyrLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Oguz SelvitopiLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Cristina TeodoropolLawrence Berkeley National Laboratory, Berkeley, CA, USA.
Leonid OlikerLawrence Berkeley National Laboratory, Berkeley, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or 'motifs' that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

Indexed as

bioinformaticshigh-performance data analyticsparallel computing

Identifiers

PMID31955674
PMCPMC7015300

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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.