Evidence map›Paper›PMID 31089679›Full record

ArticleGigaScience2019

Ultra-deep, long-read nanopore sequencing of mock microbial community standards.

Samuel M Nicholls, Joshua C Quick, Shuiquan Tang, Nicholas J Loman

Abstract read
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers.

0numbers the graph read from it
0cells of the map it votes in
157citing 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

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3 · Its place in the literature

Who cites it

157 citing papers in PubMed.

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97 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

4 authors.

Samuel M NichollsInstitute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK.
Joshua C QuickInstitute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK.
Shuiquan TangZymo Research Corporation, 17062 Murphy Ave., Irvine, CA 92614, USA.
Nicholas J LomanInstitute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, B15 2TT, UK.

Funding

Department of HealthMedical Research Council MR/L015080/1Medical Research Council MR/M501621/1
6 · The paper itself

Abstract

backgroundLong sequencing reads are information-rich: aiding de novo assembly and reference mapping, and consequently have great potential for the study of microbial communities. However, the best approaches for analysis of long-read metagenomic data are unknown. Additionally, rigorous evaluation of bioinformatics tools is hindered by a lack of long-read data from validated samples with known composition.

findingsWe sequenced 2 commercially available mock communities containing 10 microbial species (ZymoBIOMICS Microbial Community Standards) with Oxford Nanopore GridION and PromethION. Both communities and the 10 individual species isolates were also sequenced with Illumina technology. We generated 14 and 16 gigabase pairs from 2 GridION flowcells and 150 and 153 gigabase pairs from 2 PromethION flowcells for the evenly distributed and log-distributed communities, respectively. Read length N50 ranged between 5.3 and 5.4 kilobase pairs over the 4 sequencing runs. Basecalls and corresponding signal data are made available (4.2 TB in total). Alignment to Illumina-sequenced isolates demonstrated the expected microbial species at anticipated abundances, with the limit of detection for the lowest abundance species below 50 cells (GridION). De novo assembly of metagenomes recovered long contiguous sequences without the need for pre-processing techniques such as binning.

conclusionsWe present ultra-deep, long-read nanopore datasets from a well-defined mock community. These datasets will be useful for those developing bioinformatics methods for long-read metagenomics and for the validation and comparison of current laboratory and software pipelines.

Indexed as

MetagenomeMetagenomicsMicrobiotaNanopore SequencingReference Standardsbenchmarkbioinformaticsde novo assemblyIlluminametagenomicsmock communitynanoporereal-time sequencingsingle-molecule sequencing

Identifiers

PMID31089679
PMCPMC6520541

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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.