Paired VH:VL Analysis of Naïve B Cell Repertoires and Comparison to Antigen-Experienced B Cell Repertoires in Healthy Human Donors

DeKosky, Brandon J.

doi:10.1007/978-3-319-58518-5_4

Cited by 2 publications

(3 citation statements)

References 35 publications

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“…Might, therefore, recurrent neural network approaches be more appropriate to model the immune repertoire sequence space given their ability to account for sequence interactions of arbitrary length (81,82)? (iii) Finally, we still have only very superficial insight into the biological diversity of antigen-specific repertoires and the combination rules of IGV L /IGV H and TCRVa/TCRVb chains due to the lack of large-scale data (74,(83)(84)(85)(86). Once more extensive data has become available, can we leverage machine learning to uncover the underlying structure of antigen-specific repertoires and the prediction rules of chain pairing?…”

Section: Measuring Immune Repertoire Diversitymentioning

confidence: 99%

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

et al. 2018

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The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.

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Section: Measuring Immune Repertoire Diversitymentioning

confidence: 99%

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

et al. 2018

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“…Currently, it remains unclear whether the pairing rules that combine two chains in one receptor follow a uniform distribution or not, and until now, there are no studies that were able to infer the pairing rules (Dupic et al 2019; Jayaram, Bhowmick, and Martin 2012; B. DeKosky 2017; B. J. DeKosky et al 2016).…”

Section: Methodsmentioning

confidence: 99%

“…Recently developed single-cell AIRR-seq technologies enable accurate identification of paired AIRs (113,114). Currently, it remains unclear whether the pairing rules that combine two chains in one receptor follow a uniform distribution or not, and until now, there are no studies that were able to infer the pairing rules (115)(116)(117)(118). Furthermore, there is little evidence of major structural constraints in chain pairing (119).…”

Section: Representation Of Immune Eventsmentioning

confidence: 99%

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Chernigovskaya,

Pavlović,

Kanduri

et al. 2023

Preprint

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Machine-learning methods (ML) have shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML approaches where experimental data is inaccessible or insufficient as of yet. The challenge for simulated data to be useful is the ability to incorporate key features observed in experimental repertoires. These features, such as complex antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a modular software suite, which simulates AIRR data for the development and benchmarking of AIRR-based machine learning. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO simulation supports the development and validation of AIRR-ML methods: (1) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (2) how immune information co-occurring in the same AIRs have an impact on the performance of conventional receptor-level encoding and repertoire-level classification approaches. The LIgO software guides the advancement and assessment of interpretable AIRR-ML methods.

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Paired VH:VL Analysis of Naïve B Cell Repertoires and Comparison to Antigen-Experienced B Cell Repertoires in Healthy Human Donors

Cited by 2 publications

References 35 publications

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

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