Evidence map›Paper›PMID 25665577›Full record

ArticleGenome research2015

Accurate, multi-kb reads resolve complex populations and detect rare microorganisms.

Itai Sharon, Michael Kertesz, Laura A Hug, Dmitry Pushkarev, Timothy A Blauwkamp, Cindy J Castelle, Mojgan Amirebrahimi, Brian C Thomas, David Burstein, Susannah G Tringe and 2 more

Open access · bronzeAbstract read
In one paragraph

Article in Genome research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.

0numbers the graph read from it
0cells of the map it votes in
74citing 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

74 citing papers in PubMed, 151 citations in OpenAlex.

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14 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

12 authors at 6 institutions in 1 country.

Itai SharonDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA;
Michael KerteszDepartment of Bioengineering, Stanford University and Howard Hughes Medical Institute, Stanford University, Stanford, California 94305, USA;
Laura A HugDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA;
Dmitry PushkarevDepartment of Physics, Stanford University, Stanford, California 94305, USA;
Timothy A BlauwkampIllumina Inc. Technology Development, Hayward, California 94545, USA;
Cindy J CastelleDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA;
Mojgan AmirebrahimiDepartment of Energy, Joint Genome Institute, Walnut Creek, California 94598, USA;
Brian C ThomasDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA;
David BursteinDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA;
Susannah G TringeDepartment of Energy, Joint Genome Institute, Walnut Creek, California 94598, USA;
Kenneth H WilliamsLawrence Berkeley National Laboratory, Berkeley, California 94720, USA.
Jillian F BanfieldDepartment of Earth and Planetary Science, University of California, Berkeley, Berkeley, California 94720, USA; Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA jbanfield@berkeley.edu.
Planetary Science Institute · USJoint Genome Institute · USLawrence Berkeley National Laboratory · USHoward Hughes Medical Institute · USIllumina (United States) · USStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate evaluation of microbial communities is essential for understanding global biogeochemical processes and can guide bioremediation and medical treatments. Metagenomics is most commonly used to analyze microbial diversity and metabolic potential, but assemblies of the short reads generated by current sequencing platforms may fail to recover heterogeneous strain populations and rare organisms. Here we used short (150-bp) and long (multi-kb) synthetic reads to evaluate strain heterogeneity and study microorganisms at low abundance in complex microbial communities from terrestrial sediments. The long-read data revealed multiple (probably dozens of) closely related species and strains from previously undescribed Deltaproteobacteria and Aminicenantes (candidate phylum OP8). Notably, these are the most abundant organisms in the communities, yet short-read assemblies achieved only partial genome coverage, mostly in the form of short scaffolds (N50 = ∼ 2200 bp). Genome architecture and metabolic potential for these lineages were reconstructed using a new synteny-based method. Analysis of long-read data also revealed thousands of species whose abundances were <0.1% in all samples. Most of the organisms in this "long tail" of rare organisms belong to phyla that are also represented by abundant organisms. Genes encoding glycosyl hydrolases are significantly more abundant than expected in rare genomes, suggesting that rare species may augment the capability for carbon turnover and confer resilience to changing environmental conditions. Overall, the study showed that a diversity of closely related strains and rare organisms account for a major portion of the communities. These are probably common features of many microbial communities and can be effectively studied using a combination of long and short reads.

Indexed as

Bacterial ProteinsBase SequenceBiodiversityChloroflexiDeltaproteobacteriaDNA, BacterialGenome, BacterialGeologic SedimentsGlucoseHydrolasesMetagenomicsMicrobial ConsortiaSequence Analysis, DNABacterial ProteinsDNA, BacterialGlucoseHydrolases

Identifiers

PMID25665577
PMCPMC4381525
OpenAlexW2146384743

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.