Evidence map›Paper›PMID 25586223›Full record

ArticleNucleic acids research2015

An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data.

Xutao Deng, Samia N Naccache, Terry Ng, Scot Federman, Linlin Li, Charles Y Chiu, Eric L Delwart

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 175 papers.

0numbers the graph read from it
0cells of the map it votes in
175citing 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

175 citing papers in PubMed.

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  4. Complete genome sequences of twoMicrobiology resource announcements · 2026
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115 more citing papers are in PubMed but not listed here.

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

7 authors.

Xutao DengBlood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA xdeng@bloodsystems.org.
Samia N NaccacheDepartment of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA.
Terry NgBlood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.
Scot FedermanDepartment of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA.
Linlin LiBlood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.
Charles Y ChiuDepartment of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA 94107, USA Department of Medicine, Division of Infectious Diseases, UCSF, San Francisco, CA 94143, USA.
Eric L DelwartBlood Systems Research Institute, San Francisco, CA 94118, USA Department of Laboratory Medicine, University of California at San Francisco, San Francisco, CA 94107, USA.

Funding

Pan-Genomic Approaches for Comprehensive Blood Screening of Novel or Emerging Infectious AgentsR01HL105704 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHIU, CHARLES YEN · 2011 to 2020
$5.9M
Metagenomic detection of emerging viruses in the blood supplyR01HL105770 · NHLBI · VITALANT · PI DELWART, ERIC L · 2011 to 2015
$2.4M
NHLBI NIH HHS R01 HL105704NHLBI NIH HHS R01-HL105704NHLBI NIH HHS R01 HL105770
6 · The paper itself

Abstract

Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free (de novo) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.

Indexed as

MetagenomicsBacteriaGenome, BacterialGenome, ViralHumansSequence AnalysisViruses

Identifiers

PMID25586223
PMCPMC4402509

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.