Evidence map›Paper›PMID 27465705›Full record

ArticleBMC bioinformatics2016

Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations.

Aaron Y Lee, Cecilia S Lee, Russell N Van Gelder

Abstract read
In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

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  15. A Computational Approach Using Bioinformatics to Screening Drug Targets forEvidence-based complementary and alternative medicine : eCAM · 2018
    Article
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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

3 authors.

Aaron Y LeeDepartment of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA. leeay@uw.edu.
Cecilia S LeeDepartment of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA.
Russell N Van GelderDepartment of Ophthalmology, University of Washington School of Medicine, Box 359608, 325 Ninth Avenue, Seattle, WA, 98104, USA.

Funding

Vision Research CoreP30EY001730 · NEI · UNIVERSITY OF WASHINGTON · PI MAUREEN E NEITZ · 1985 to 2026
$23.5M
Diversity and Dynamic Stability of the Ocular Surface MicrobiomeR01EY022038 · NEI · UNIVERSITY OF WASHINGTON · PI SHESTOPALOV, VALERY I, VAN GELDER, RUSSELL N. · 2012 to 2016
$3.2M
The ocular surface microbiome in potentially infectious ophthalmic diseaseK23EY024921 · NEI · UNIVERSITY OF WASHINGTON · PI LEE, CECILIA SUNGMIN · 2015 to 2019
$991k
NEI NIH HHS K23 EY024921NEI NIH HHS R01 EY022038
6 · The paper itself

Abstract

backgroundNext generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results.

resultsAfter retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 10(11) base pairs of 3.3 × 10(6) sequences from 9.25 × 10(5) species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 10(6) reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK.

conclusionsSMART is the first scalable, efficient, and rapid metagenomics classification algorithm capable of matching against all the species and sequences present in the NCBI GenBank and allows for a single step classification of microorganisms as well as large plant, mammalian, or invertebrate genomes from which the metagenomic sample may have been derived.

Indexed as

AlgorithmsDatabases, Nucleic AcidHeuristicsHigh-Throughput Nucleotide SequencingHumansMetagenomicsSequence Analysis, DNASoftware

Identifiers

PMID27465705
PMCPMC4963998

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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.