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

Rubelt, Florian; Busse, Christian E.; Bukhari, Syed Ahmad Chan; Bürckert, Jean-Philippe; Mariotti‐Ferrandiz, Encarnita; Cowell, Lindsay G.; Watson, Corey T.; Marthandan, Nishanth; Faison, William J.; Hershberg, Uri; Laserson, Uri; Corrie, Brian; Davis, Mark M.; Peters, Bjoern; Lefranc, Marie‐Paule; Scott, Jamie K.; Breden, Felix; Prak, Eline T. Luning; Kleinstein, Steven H.

doi:10.1038/ni.3873

Cited by 156 publications

(155 citation statements)

References 24 publications

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“…At the 2015 AIRR Community Meeting, Working Groups were formed to address three priority areas: (1) Minimal Standards for the publication and sharing of AIRR-seq datasets (26); (2) Tools and Resources to facilitate the comparison of AIRR-seq datasets; and (3) a Common Repository to establish an AIRR Data Commons. These Working Groups are dynamic and often collaborate with each other, as methods evolve and applications of standards in one area (for example, metadata standards) impact other areas (data repository requirements).…”

Section: Airr-seq Data: Challenges and Community Responsementioning

confidence: 99%

“…One of the most recent outcomes of the AIRR Community and its working groups has been the publication of the Minimal Information for AIRR-seq data (MiAIRR) standard (26). The MiAIRR standard has been approved by the AIRR Community and is considered the set of metadata that is minimally necessary to interpret and compare AIRR-seq experiments and data sets.…”

Section: Ireceptor - Towards An Airr-seq Data Commonsmentioning

confidence: 99%

“…In 2016, iReceptor received funding to continue the develop- One of the most recent outcomes of the AIRR Community and its working groups has been the publication of the Minimal Information for AIRR-seq data (MiAIRR) standard. 26 The MiAIRR standard has been approved by the AIRR Community and is considered the set of metadata that is minimally necessary to interpret and compare AIRR-seq experiments and data sets. It consists of six high high-level classes of metadata that provide information about different aspects of an AIRR-seq-based study: iReceptor has made extensive use of the MiAIRR standardization efforts to direct its development.…”

Section: The Present-ireceptor V20mentioning

confidence: 99%

See 2 more Smart Citations

iReceptor: A platform for querying and analyzing antibody/B‐cell and T‐cell receptor repertoire data across federated repositories

Corrie

Marthandan

Zimonja

et al. 2018

Immunological Reviews

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144

151

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Next-generation sequencing allows the characterization of the adaptive immune receptor repertoire (AIRR) in exquisite detail. These large-scale AIRR-seq data sets have rapidly become critical to vaccine development, understanding the immune response in autoimmune and infectious disease, and monitoring novel therapeutics against cancer. However, at present there is no easy way to compare these AIRR-seq data sets across studies and institutions. The ability to combine and compare information for different disease conditions will greatly enhance the value of AIRR-seq data for improving biomedical research and patient care. The iReceptor Data Integration Platform (gateway.ireceptor.org) provides one implementation of the AIRR Data Commons envisioned by the AIRR Community (airr-community.org), an initiative that is developing protocols to facilitate sharing and comparing AIRR-seq data. The iReceptor Scientific Gateway links distributed (federated) AIRR-seq repositories, allowing sequence searches or metadata queries across multiple studies at multiple institutions, returning sets of sequences fulfilling specific criteria. We present a review of the development of iReceptor, and how it fits in with the general trend toward sharing genomic and health data, and the development of standards for describing and reporting AIRR-seq data. Researchers interested in integrating their repositories of AIRR-seq data into the iReceptor Platform are invited to contact support@ireceptor.org.

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Section: Airr-seq Data: Challenges and Community Responsementioning

confidence: 99%

Section: Ireceptor - Towards An Airr-seq Data Commonsmentioning

confidence: 99%

Section: The Present-ireceptor V20mentioning

confidence: 99%

See 1 more Smart Citation

iReceptor: A platform for querying and analyzing antibody/B‐cell and T‐cell receptor repertoire data across federated repositories

Corrie

Marthandan

Zimonja

et al. 2018

Immunological Reviews

Self Cite

144

151

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“…Moreover, sharing of TCR data maximizes the data available to researchers to undertake systems immunology studies using meta-analysis strategies. With this public sharing of data comes the challenges of establishing standards of practice for the acquisition, annotation, and sharing of data, which are being addressed by the Adaptive Immune Receptor Repertoire (AIRR) Community [22,23]. TCR repertoire studies also benefit from the increasing amounts of other types of data that are being generated by immunological investigations.…”

Section: Increasing Resources For Studying Tcr Repertoiresmentioning

confidence: 99%

The expanding role of systems immunology in decoding the T cell receptor repertoire

Venturi¹

2018

Current Opinion in Systems Biology

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T cells play a crucial role in the immune system’s defense against many infectious diseases, including persistent infections for which no effective vaccines currently exist. The T cell component of the adaptive immune system is highly complex involving a constantly evolving landscape of various inter-related T cell populations. These T cell populations are characterized by their phenotypic and functional properties as well as the collection, or repertoire, of T cell receptors (TCR) that mediate T cell recognition of antigenic peptides derived from pathogens. Understanding the various processes and factors that impact the development and evolution of the broader T cell repertoire available to recognize and respond to pathogens and the characteristics of antigen-experienced T cell repertoires associated with effective immune control of pathogens is critical to the rational design of T cell-based vaccines and therapies. In this article we discuss, using examples of recent research, the promise that systems immunology approaches, involving quantitative analysis and mathematical and computational modeling of immunological data, hold for decoding the complex TCR repertoire system in the current era of advancing technologies.

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

Cited by 156 publications

References 24 publications

iReceptor: A platform for querying and analyzing antibody/B‐cell and T‐cell receptor repertoire data across federated repositories

iReceptor: A platform for querying and analyzing antibody/B‐cell and T‐cell receptor repertoire data across federated repositories

The expanding role of systems immunology in decoding the T cell receptor repertoire

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

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