Adaptive Immune Receptor Repertoire Community recommendations for sharing immune-repertoire sequencing data

Abstract VDJbase is a publicly available database that offers easy searching of data describing the complete sets of gene sequences (genotypes and haplotypes) inferred from adaptive immune receptor repertoire sequencing datasets. VDJbase is designed to act as a resource that will allow the scientific community to explore the genetic variability of the immunoglobulin (Ig) and T cell receptor (TR) gene loci. It can also assist in the investigation of Ig- and TR-related genetic predispositions to diseases. Our database includes web-based query and online tools to assist in visualization and analysis of the genotype and haplotype data. It enables users to detect those alleles and genes that are significantly over-represented in a particular population, in terms of genotype, haplotype and gene expression. The database website can be freely accessed at https://www.vdjbase.org/, and no login is required. The data and code use creative common licenses and are freely downloadable from https://bitbucket.org/account/user/yaarilab/projects/GPHP.

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Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

eLife ◽

10.7554/elife.66274 ◽

2021 ◽

Vol 10 ◽

Author(s):

Johannes Trück ◽

Anne Eugster ◽

Pierre Barennes ◽

Christopher M Tipton ◽

Eline T Luning Prak ◽

...

Keyword(s):

Immune System ◽

High Throughput ◽

Working Group ◽

Current Status ◽

Immune Receptor ◽

Adaptive Immune ◽

Receptor Repertoire ◽

Repertoire Sequencing ◽

Diagnostic Applications ◽

Future Work

Use of adaptive immune receptor repertoire sequencing (AIRR-seq) has become widespread, providing new insights into the immune system with potential broad clinical and diagnostic applications. However, like many high-throughput technologies, it comes with several problems, and the AIRR Community was established to understand and help solve them. We, the AIRR Community’s Biological Resources Working Group, have surveyed scientists about the need for standards and controls in generating and annotating AIRR-seq data. Here, we review the current status of AIRR-seq, provide the results of our survey, and based on them, offer recommendations for developing AIRR-seq standards and controls, including future work.

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The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

Immunological Reviews ◽

10.1111/imr.12664 ◽

2018 ◽

Vol 284 (1) ◽

pp. 148-166 ◽

Cited By ~ 3

Author(s):

Branden J. Olson ◽

Frederick A. Matsen

Keyword(s):

Immune Receptor ◽

Adaptive Immune ◽

Receptor Repertoire ◽

Repertoire Analysis

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The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer

Cancer Research ◽

10.1158/0008-5472.can-19-1457 ◽

2019 ◽

Vol 80 (4) ◽

pp. 643-654 ◽

Cited By ~ 1

Author(s):

Lindsay G. Cowell

Keyword(s):

Therapeutic Potential ◽

Immune Receptor ◽

Adaptive Immune ◽

Receptor Repertoire

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PIRD: Pan Immune Repertoire Database

Bioinformatics ◽

10.1093/bioinformatics/btz614 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wei Zhang ◽

Longlong Wang ◽

Ke Liu ◽

Xiaofeng Wei ◽

Kai Yang ◽

...

Keyword(s):

Data Storage ◽

High Throughput Sequencing ◽

Homo Sapiens ◽

Adaptive Immune System ◽

B Cell Receptors ◽

Sequencing Data ◽

Immune Repertoire ◽

Adaptive Immune ◽

Potential Applications ◽

Antibody Drug

Abstract Motivation T and B cell receptors (TCRs and BCRs) play a pivotal role in the adaptive immune system by recognizing an enormous variety of external and internal antigens. Understanding these receptors is critical for exploring the process of immunoreaction and exploiting potential applications in immunotherapy and antibody drug design. Although a large number of samples have had their TCR and BCR repertoires sequenced using high-throughput sequencing in recent years, very few databases have been constructed to store these kinds of data. To resolve this issue, we developed a database. Results We developed a database, the Pan Immune Repertoire Database (PIRD), located in China National GeneBank (CNGBdb), to collect and store annotated TCR and BCR sequencing data, including from Homo sapiens and other species. In addition to data storage, PIRD also provides functions of data visualization and interactive online analysis. Additionally, a manually curated database of TCRs and BCRs targeting known antigens (TBAdb) was also deposited in PIRD. Availability and implementation PIRD can be freely accessed at https://db.cngb.org/pird.

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Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data

Frontiers in Immunology ◽

10.3389/fimmu.2017.01418 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 54

Author(s):

Felix Breden ◽

Eline T. Luning Prak ◽

Bjoern Peters ◽

Florian Rubelt ◽

Chaim A. Schramm ◽

...

Keyword(s):

Immune Receptor ◽

Adaptive Immune ◽

Receptor Repertoire

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Performance-optimized partitioning of clonotypes from high-throughput immunoglobulin repertoire sequencing data

10.1101/175315 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nima Nouri ◽

Steven H. Kleinstein

Keyword(s):

Sensitivity And Specificity ◽

High Throughput ◽

Hamming Distance ◽

Somatic Hypermutation ◽

Simulated Data ◽

Automate Method ◽

Sequencing Data ◽

Single Linkage ◽

Adaptive Immune ◽

Repertoire Sequencing

AbstractMotivationDuring adaptive immune responses, activated B cells expand and undergo somatic hypermutation of their immunoglobulin (Ig) receptor, forming a clone of diversified cells that can be related back to a common ancestor. Identification of B cell clonotypes from high-throughput Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data relies on computational analysis. Recently, we proposed an automate method to partition sequences into clonal groups based on single-linkage clustering of the Ig receptor junction region with length-normalized hamming distance metric. This method could identify clonally-related sequences with high confidence on several benchmark experimental and simulated data sets. However, this approach was computationally expensive, and unable to provide estimates of accuracy for new data. Here, a new method is presented that address this computational bottleneck and also provides a study-specific estimation of performance, including sensitivity and specificity. The method uses a finite mixture modeling fitting procedure for learning the parameters of two univariate curves which fit the bimodal distributions of the distance vector between pairs of sequences. These distribution are used to estimate the performance of different threshold choices for partitioning sequences into clonotypes. These performance estimates are validated using simulated and experimental datasets. With this method, clonotypes can be identified from AIRR-seq data with sensitivity and specificity profiles that are user-defined based on the overall goals of the study.AvailabilitySource code is freely available at the Immcantation Portal: www.immcantation.com under the CC BY-SA 4.0 [email protected]

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