Evidence map›Paper›PMID 35622339›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Data Sharing and Reuse: A Method by the AIRR Community.

Brian D Corrie, Scott Christley, Christian E Busse, Lindsay G Cowell, Kira C M Neller, Florian Rubelt, Nicholas Schwab, AIRR Community

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. 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

8 authors.

Brian D CorrieBiological Sciences, Simon Fraser University, Burnaby, BC, Canada. bcorrie@sfu.ca.
Scott ChristleyDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA. scott.christley@utsouthwestern.edu.
Christian E BusseGerman Cancer Research Center (DKFZ), Heidelberg, Germany.
Lindsay G CowellDepartment of Population and Data Sciences, UT Southwestern Medical Center, Dallas, TX, USA.
Kira C M NellerHealth Sciences, Simon Fraser University, Burnaby, BC, Canada.
Florian RubeltRoche Sequencing Solutions, Roche, Pleasanton, CA, USA.
Nicholas SchwabDepartment of Neurology with Institute of Translational Neurology, University of Muenster, Muenster, Germany.
AIRR Community

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.

Indexed as

Information DisseminationResearch DesignHigh-Throughput Nucleotide SequencingHumansReproducibility of ResultsWorkflowAIRR-seqB-cell receptorData reuseData sharingFAIR dataImmunoglobulinT-cell receptor

Identifiers

PMID35622339
PMCPMC9761493

What OpenQuestion holds

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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.