Evidence map›Paper›PMID 31971941›Full record

ArticlePLoS computational biology2020

An educational guide for nanopore sequencing in the classroom.

Alex N Salazar, Franklin L Nobrega, Christine Anyansi, Cristian Aparicio-Maldonado, Ana Rita Costa, Anna C Haagsma, Anwar Hiralal, Ahmed Mahfouz, Rebecca E McKenzie, Teunke van Rossum and 2 more

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Nanopore sequencing in non-human forensic genetics.Emerging topics in life sciences · 2021
    Review
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. 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

12 authors.

Alex N SalazarDelft Bioinformatics Laboratory, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-8453-8769
Franklin L NobregaKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-8238-1083
Christine AnyansiDelft Bioinformatics Laboratory, Delft University of Technology, Delft, Netherlands.ORCID 0000-0003-4208-0697
Cristian Aparicio-MaldonadoKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.
Ana Rita CostaKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.
Anna C HaagsmaKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.
Anwar HiralalKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.ORCID 0000-0003-3797-6616
Ahmed MahfouzDelft Bioinformatics Laboratory, Delft University of Technology, Delft, Netherlands.ORCID 0000-0001-8601-2149
Rebecca E McKenzieKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.
Teunke van RossumKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-5219-1799
Stan J J BrounsKavli Institute of Nanoscience, Department of Bionanoscience, Delft University of Technology, Delft, Netherlands.
Thomas AbeelDelft Bioinformatics Laboratory, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-7205-7431

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The last decade has witnessed a remarkable increase in our ability to measure genetic information. Advancements of sequencing technologies are challenging the existing methods of data storage and analysis. While methods to cope with the data deluge are progressing, many biologists have lagged behind due to the fast pace of computational advancements and tools available to address their scientific questions. Future generations of biologists must be more computationally aware and capable. This means they should be trained to give them the computational skills to keep pace with technological developments. Here, we propose a model that bridges experimental and bioinformatics concepts using the Oxford Nanopore Technologies (ONT) sequencing platform. We provide both a guide to begin to empower the new generation of educators, scientists, and students in performing long-read assembly of bacterial and bacteriophage genomes and a standalone virtual machine containing all the required software and learning materials for the course.

Indexed as

Nanopore SequencingComputational BiologyHumansSoftware

Identifiers

PMID31971941
PMCPMC6977714

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.