Evidence map›Paper›PMID 30814994›Full record

ArticleFrontiers in immunology2019

Identification of Subject-Specific Immunoglobulin Alleles From Expressed Repertoire Sequencing Data.

Daniel Gadala-Maria, Moriah Gidoni, Susanna Marquez, Jason A Vander Heiden, Justin T Kos, Corey T Watson, Kevin C O'Connor, Gur Yaari, Steven H Kleinstein

Abstract read
In one paragraph

Article 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 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

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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

9 authors.

Daniel Gadala-MariaInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.
Moriah GidoniBioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.
Susanna MarquezDepartment of Pathology, Yale School of Medicine, Yale University, New Haven, CT, United States.
Jason A Vander HeidenDepartment of Neurology, Yale School of Medicine, Yale University, New Haven, CT, United States.
Justin T KosDepartment of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States.
Corey T WatsonDepartment of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY, United States.
Kevin C O'ConnorDepartment of Neurology, Yale School of Medicine, Yale University, New Haven, CT, United States.
Gur YaariBioengineering Program, Faculty of Engineering, Bar-Ilan University, Ramat Gan, Israel.
Steven H KleinsteinInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States.

Funding

An ethnically diverse genomic reference resource for the human heavy and light chain immunoglobulin lociR24AI138963 · NIAID · UNIVERSITY OF LOUISVILLE · PI Melissa Laird Smith, Corey Taylor Watson · 2018 to 2026
$5.5M
Mechanisms of autoimmunity in myasthenia gravisR01AI114780 · NIAID · YALE UNIVERSITY · PI Kevin C O'Connor · 2016 to 2026
$4.7M
COMPUTATIONAL TOOLS FOR THE ANALYSIS OF HIGH-THROUGHPUT IMMUNOGLOBULIN SEQUENCING EXPERIMENTSR01AI104739 · NIAID · YALE UNIVERSITY · PI KLEINSTEIN, STEVEN H. · 2014 to 2022
$3.6M
Population level characterization of human immunoglobulin heavy chain germline variants with impacts on variability in the Ab repertoire of healthy adultsR21AI142590 · NIAID · UNIVERSITY OF LOUISVILLE · PI MARASCO, WAYNE A., WATSON, COREY TAYLOR · 2019 to 2020
$477k
NIAID NIH HHS R01 AI104739NIAID NIH HHS R01 AI114780NIAID NIH HHS R21 AI142590NIAID NIH HHS R24 AI138963
6 · The paper itself

Abstract

The adaptive immune receptor repertoire (AIRR) contains information on an individuals' immune past, present and potential in the form of the evolving sequences that encode the B cell receptor (BCR) repertoire. AIRR sequencing (AIRR-seq) studies rely on databases of known BCR germline variable (V), diversity (D), and joining (J) genes to detect somatic mutations in AIRR-seq data via comparison to the best-aligning database alleles. However, it has been shown that these databases are far from complete, leading to systematic misidentification of mutated positions in subsets of sample sequences. We previously presented TIgGER, a computational method to identify subject-specific V gene genotypes, including the presence of novel V gene alleles, directly from AIRR-seq data. However, the original algorithm was unable to detect alleles that differed by more than 5 single nucleotide polymorphisms (SNPs) from a database allele. Here we present and apply an improved version of the TIgGER algorithm which can detect alleles that differ by any number of SNPs from the nearest database allele, and can construct subject-specific genotypes with minimal prior information. TIgGER predictions are validated both computationally (using a leave-one-out strategy) and experimentally (using genomic sequencing), resulting in the addition of three new immunoglobulin heavy chain V (IGHV) gene alleles to the IMGT repertoire. Finally, we develop a Bayesian strategy to provide a confidence estimate associated with genotype calls. All together, these methods allow for much higher accuracy in germline allele assignment, an essential step in AIRR-seq studies.

Indexed as

AlgorithmsAllelesBayes TheoremGenotypeHumansImmunoglobulinsMyasthenia GravisSequence Analysis, DNAImmunoglobulinsAIRR-seqalleleantibodiesBCRsomatic hypermutation

Identifiers

PMID30814994
PMCPMC6381938

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Registered trials

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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.