AIRR Community Standardized Representations for Annotated Immune Repertoires

Heiden, Jason A. Vander; Marquez, Susanna; Marthandan, Nishanth; Bukhari, Syed; Busse, Christian E.; Corrie, Brian; Hershberg, Uri; Kleinstein, Steven H.; Matsen, F. A.; Ralph, D.; Rosenfeld, Aaron M.; Schramm, Chaim A.; Christley, Scott; Laserson, Uri

doi:10.3389/fimmu.2018.02206

Cited by 78 publications

(73 citation statements)

References 41 publications

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“…This implementation of SCOPer is freely available as part of the Immcantation framework (www.immcantation.org) under the AGPLv3 license. The input and output formats of SCOPer conform to the Change-O [48] and AIRR [21] file standard, and thus the method can be used seamlessly as part of the Immcantation tool suite, including methods for B cell clonal lineage reconstruction, lineage topology analysis, clonal diversity analysis, and other advanced repertoire analyses linked to the clonal landscape.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Nouri

Kleinstein

2020

PLoS Comput Biol

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Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their B cell receptor (BCR) sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse junction region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the junction sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture junction similarity and shared mutations, and combine these in a spectral clustering framework to infer the BCR clonal relationships. Using both simulated and experimental data, we show that this model improves both the sensitivity and specificity for identifying B cell clones. Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the AGPLv3 license.

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Section: Plos Computational Biologymentioning

confidence: 99%

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Nouri

Kleinstein

2020

PLoS Comput Biol

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“…This implementation of SCOPer is freely available as part of the Immcantation framework (www.immcantation.org) under the CC BY-SA 4.0 license. The input and output formats of SCOPer conform to the Change-O and AIRR (Vander Heiden et al, 2018) file standard, and thus the method can be used seamlessly as part of the Immcantation tool suite, including methods for B cell clonal lineage reconstruction, lineage topology analysis, clonal diversity analysis, and other advanced repertoire analyses linked to the clonal landscape.…”

Section: Resultsmentioning

confidence: 99%

“…Shared and unique mutations are marked at each branch by horizontal lines and arrowheadlines, respectively. and Joshi, 2015; Rubelt et al, 2017;Vander Heiden et al, 2018). However, clonal relationships are not directly measured, but they must be computationally inferred.…”

Section: Introductionmentioning

confidence: 99%

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Nouri

Kleinstein

2019

Preprint

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Motivation: Adaptive immune receptor repertoire sequencing (AIRR-Seq) offers the possibility of identifying and tracking B cell clonal expansions during adaptive immune responses. Members of a B cell clone are descended from a common ancestor and share the same initial V(D)J rearrangement, but their BCR sequence may differ due to the accumulation of somatic hypermutations (SHMs). Clonal relationships are learned from AIRR-seq data by analyzing the BCR sequence, with the most common methods focused on the highly diverse CDR3 region. However, clonally related cells often share SHMs which have been accumulated during affinity maturation. Here, we investigate whether shared SHMs in the V and J segments of the BCR can be leveraged along with the CDR3 sequence to improve the ability to identify clonally related sequences. We develop independent distance functions that capture shared mutations and CDR3 similarity, and combine these in a spectral clustering framework. Using simulated data, we show that this model improves both the sensitivity and specificity for identifying clonal relationships. Availability: Source code for this method is freely available in the SCOPer (Spectral Clustering for clOne Partitioning) R package (version 0.2 or newer) in the Immcantation framework: www.immcantation.org under the CC BY-SA 4.0 license.

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“…fields and definitions that comply with the Adaptive Immune Receptor Repertoire (AIRR) Community Rearrangement schema [36]. A reproducible installation procedure of sumrep is available using Docker [3].…”

Section: Methodsmentioning

confidence: 99%

sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation

Olson

Moghimi

Schramm

et al. 2019

Preprint

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The adaptive immune system generates an incredible diversity of antigen receptors for B and T cells to keep dangerous pathogens at bay. The DNA sequences coding for these receptors arise by a complex recombination process followed by a series of productivity-based filters, as well as affinity maturation for B cells, giving considerable diversity to the circulating pool of receptor sequences. Although these datasets hold considerable promise for medical and public health applications, the complex structure of the resulting adaptive immune receptor repertoire sequencing (AIRR-seq) datasets makes analysis difficult. In this paper we introduce sumrep, an R package that efficiently performs a wide variety of repertoire summaries and comparisons, and show how sumrep can be used to perform model validation. We find that summaries vary in their ability to differentiate between datasets, although many are able to distinguish between covariates such as donor, timepoint, and cell type for BCR and TCR repertoires. We show that deletion and insertion lengths resulting from V(D)J recombination tend to be more discriminative characterizations of a repertoire than summaries that describe the amino acid composition of the CDR3 region. We also find that state-of-the-art generative models excel at recapitulating gene usage and recombination statistics in a given experimental repertoire, but struggle to capture many physiochemical properties of real repertoires.

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AIRR Community Standardized Representations for Annotated Immune Repertoires

Cited by 78 publications

References 41 publications

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data

sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation

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