Evidence map›Paper›PMID 26637598›Full record

ArticleApplied and environmental microbiology2016

Single-Cell-Genomics-Facilitated Read Binning of Candidate Phylum EM19 Genomes from Geothermal Spring Metagenomes.

Eric D Becraft, Jeremy A Dodsworth, Senthil K Murugapiran, J Ingemar Ohlsson, Brandon R Briggs, Jad Kanbar, Iwijn De Vlaminck, Stephen R Quake, Hailiang Dong, Brian P Hedlund and 1 more

Abstract read
In one paragraph

Article in Applied and environmental microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Microbial single-cell omics: the crux of the matter.Applied microbiology and biotechnology · 2020
    Review
  5. Article
  6. Article
  7. Single cell ecology.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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

11 authors.

Eric D BecraftDepartment of Biological Sciences, Northern Illinois University, DeKalb, Illinois, USA.
Jeremy A DodsworthSchool of Life Sciences, University of Nevada, Las Vegas, Nevada, USA.
Senthil K MurugapiranSchool of Life Sciences, University of Nevada, Las Vegas, Nevada, USA.
J Ingemar OhlssonDepartment of Biological Sciences, Northern Illinois University, DeKalb, Illinois, USA.
Brandon R BriggsDepartment of Geology and Environmental Earth Science, Miami University, Oxford, Ohio, USA.
Jad KanbarDepartments of Bioengineering and Applied Physics, Stanford University and the Howard Hughes Medical Institute, Stanford, California, USA.
Iwijn De VlaminckDepartments of Bioengineering and Applied Physics, Stanford University and the Howard Hughes Medical Institute, Stanford, California, USA.
Stephen R QuakeDepartments of Bioengineering and Applied Physics, Stanford University and the Howard Hughes Medical Institute, Stanford, California, USA.
Hailiang DongDepartment of Geology and Environmental Earth Science, Miami University, Oxford, Ohio, USA.
Brian P HedlundSchool of Life Sciences, University of Nevada, Las Vegas, Nevada, USA.
Wesley D SwingleyDepartment of Biological Sciences, Northern Illinois University, DeKalb, Illinois, USA wswingley@niu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The vast majority of microbial life remains uncatalogued due to the inability to cultivate these organisms in the laboratory. This "microbial dark matter" represents a substantial portion of the tree of life and of the populations that contribute to chemical cycling in many ecosystems. In this work, we leveraged an existing single-cell genomic data set representing the candidate bacterial phylum "Calescamantes" (EM19) to calibrate machine learning algorithms and define metagenomic bins directly from pyrosequencing reads derived from Great Boiling Spring in the U.S. Great Basin. Compared to other assembly-based methods, taxonomic binning with a read-based machine learning approach yielded final assemblies with the highest predicted genome completeness of any method tested. Read-first binning subsequently was used to extract Calescamantes bins from all metagenomes with abundant Calescamantes populations, including metagenomes from Octopus Spring and Bison Pool in Yellowstone National Park and Gongxiaoshe Spring in Yunnan Province, China. Metabolic reconstruction suggests that Calescamantes are heterotrophic, facultative anaerobes, which can utilize oxidized nitrogen sources as terminal electron acceptors for respiration in the absence of oxygen and use proteins as their primary carbon source. Despite their phylogenetic divergence, the geographically separate Calescamantes populations were highly similar in their predicted metabolic capabilities and core gene content, respiring O2, or oxidized nitrogen species for energy conservation in distant but chemically similar hot springs.

Indexed as

Genome, MicrobialChinaComputational BiologyGenomicsHigh-Throughput Nucleotide SequencingHot SpringsMachine LearningMetagenomicsUnited States

Identifiers

PMID26637598
PMCPMC4751853

What OpenQuestion holds

Textmetadata
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.