Evidence map›Paper›PMID 31119088›Full record

ArticlePeerJ2019

Optimizing

Simon Roux, Gareth Trubl, Danielle Goudeau, Nandita Nath, Estelle Couradeau, Nathan A Ahlgren, Yuanchao Zhan, David Marsan, Feng Chen, Jed A Fuhrman and 5 more

Abstract read
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

15 authors.

Simon RouxDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.
Gareth TrublDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Danielle GoudeauDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.
Nandita NathDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.
Estelle CouradeauEnvironmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Nathan A AhlgrenDepartment of Biology, Clark University, Worcester, MA, United States of America.
Yuanchao ZhanInstitution of Marine and Environmental Technology, University of Maryland Center for Environmental Science, Cambridge, MD, United States of America.
David MarsanInstitution of Marine and Environmental Technology, University of Maryland Center for Environmental Science, Cambridge, MD, United States of America.
Feng ChenInstitution of Marine and Environmental Technology, University of Maryland Center for Environmental Science, Cambridge, MD, United States of America.
Jed A FuhrmanDepartment of Biological Sciences, University of Southern California, Los Angeles, CA, United States of America.
Trent R NorthenDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.
Matthew B SullivanDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Virginia I RichDepartment of Microbiology, Ohio State University, Columbus, OH, United States of America.
Rex R MalmstromDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.
Emiley A Eloe-FadroshDepartment of Energy Joint Genome Institute, Walnut Creek, CA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetagenomics has transformed our understanding of microbial diversity across ecosystems, with recent advances enabling

methodsHere we evaluate

resultsRead mapping analyses revealed that the depth of coverage within individual genomes is significantly more uneven in PCR-amplified datasets versus unamplified metagenomes, with regions of high depth of coverage enriched in short inserts. This enrichment scales with the number of PCR cycles performed, and is presumably due to preferential amplification of short inserts. Standard assembly pipelines are confounded by this type of coverage unevenness, so we evaluated other assembly options to mitigate these issues. We found that a pipeline combining read deduplication and an assembly algorithm originally designed to recover genomes from libraries generated after whole genome amplification (single-cell SPAdes) frequently improved assembly of contigs ≥10 kb by 10 to 100-fold for low input metagenomes.

conclusionsPCR-amplified metagenomes have enabled scientists to explore communities traditionally challenging to describe, including some with extremely low biomass or from which DNA is particularly difficult to extract. Here we show that a modified assembly pipeline can lead to an improved

Indexed as

Genome assemblyMetagenomicsMicrobial ecologyViral metagenomics

Identifiers

PMID31119088
PMCPMC6511391

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.