Evidence map›Paper›PMID 28978691›Full record

ArticleJournal of immunology (Baltimore, Md. : 1950)2017

Reconstructing Antibody Repertoires from Error-Prone Immunosequencing Reads.

Alexander Shlemov, Sergey Bankevich, Andrey Bzikadze, Maria A Turchaninova, Yana Safonova, Pavel A Pevzner

Abstract read
In one paragraph

Article in Journal of immunology (Baltimore, Md. : 1950), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Article
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  6. Article
  7. Antibody repertoire sequencing analysis.Acta biochimica et biophysica Sinica · 2022
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
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  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Frontiers in immunology · 2019
    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

6 authors.

Alexander ShlemovCenter for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg University, St. Petersburg, Russia 199034.ORCID 0000-0002-2317-8587
Sergey BankevichCenter for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg University, St. Petersburg, Russia 199034.ORCID 0000-0002-3651-8579
Andrey BzikadzeCenter for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg University, St. Petersburg, Russia 199034.ORCID 0000-0002-7928-7950
Maria A TurchaninovaInstitute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia 117997.
Yana SafonovaCenter for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg University, St. Petersburg, Russia 199034; isafonova@eng.ucsd.edu.
Pavel A PevznerCenter for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg University, St. Petersburg, Russia 199034.

Funding

TrainingP41GM103484 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PEVZNER, PAVEL A · 2012 to 2018
$8.7M
NIGMS NIH HHS P41 GM103484
6 · The paper itself

Abstract

Transforming error-prone immunosequencing datasets into Ab repertoires is a fundamental problem in immunogenomics, and a prerequisite for studies of immune responses. Although various repertoire reconstruction algorithms were released in the last 3 y, it remains unclear how to benchmark them and how to assess the accuracy of the reconstructed repertoires. We describe an accurate IgReC algorithm for constructing Ab repertoires from high-throughput immunosequencing datasets and a new framework for assessing the quality of reconstructed repertoires. Surprisingly, Ab repertoires constructed by IgReC from barcoded immunosequencing datasets in the blind mode (without using information about unique molecular identifiers) improved upon the repertoires constructed by the state-of-the-art tools that use barcoding. This finding suggests that IgReC may alleviate the need to generate repertoires using the barcoding technology (the workhorse of current immunogenomics efforts) because our computational approach to error correction of immunosequencing data is nearly as powerful as the experimental approach based on barcoding.

Indexed as

AlgorithmsAnimalsAntibodiesHumansSequence Analysis, ProteinAntibodies

Identifiers

PMID28978691
PMCPMC5661950

What OpenQuestion holds

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LicenceTDM
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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.