Evidence map›Paper›PMID 31114573›Full record

ReviewFrontiers in immunology2019

The Pipeline Repertoire for Ig-Seq Analysis.

Laura López-Santibáñez-Jácome, S Eréndira Avendaño-Vázquez, Carlos Fabián Flores-Jasso

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Human B Cell Receptor Repertoire Sequencing.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Data Sharing and Reuse: A Method by the AIRR Community.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review
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

3 authors.

Laura López-Santibáñez-JácomeConsorcio de Metabolismo de RNA, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
S Eréndira Avendaño-VázquezConsorcio de Metabolismo de RNA, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
Carlos Fabián Flores-JassoConsorcio de Metabolismo de RNA, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advent of high-throughput sequencing of immunoglobulin genes (Ig-Seq), the understanding of antibody repertoires and their dynamics among individuals and populations has become an exciting area of research. There is an increasing number of computational tools that aid in every step of the immune repertoire characterization. However, since not all tools function identically, every pipeline has its unique rationale and capabilities, creating a rich blend of useful features that may appear intimidating for newcomer laboratories with the desire to plunge into immune repertoire analysis to expand and improve their research; hence, all pipeline strengths and differences may not seem evident. In this review we provide a practical and organized list of the current set of computational tools, focusing on their most attractive features and differences in order to carry out the characterization of antibody repertoires so that the reader better decides a strategic approach for the experimental design, and computational pathways for the analyses of immune repertoires.

Indexed as

High-Throughput Nucleotide SequencingSoftwareV(D)J RecombinationAnimalsHumansImmunoglobulinsImmunoglobulinsantibody repertoireIg-Seqpipelinepre-processingV(D)J alignment

Identifiers

PMID31114573
PMCPMC6503734

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.