Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information

Heather, James; Spindler, Matthew J.; Alonso, Marta Herrero; Shui, Yifang; Millar, David; Johnson, D. S.; Cobbold, Mark; Hata, Aaron N.

doi:10.1093/nar/gkac190

Cited by 26 publications

(16 citation statements)

References 80 publications

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“…2E). Each AIR can be outputted in a full-length (Heather et al 2022) or CDR3 only format. Additionally, to facilitate further ML processing, the data is annotated with a binary matrix showing for every immune signal its presence in every receptor and, if so, the positions in the receptor sequence.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 2: LIgO workflow for configuring AIRR-ML benchmarking datasets (A) For initialization, a user may define general simulation parameters, such as the type of AIR (BCR or TCR), receptor-or repertoire-based simulation, paired-or single-chain receptors, type of sequence alphabet -nucleotide (nt) or amino acid (aa), output format -full-length (Heather et al 2022) or CDR3 only. For the receptor-based simulation, a number of generated AIRs and a set of immune signals with their frequencies in the final dataset are defined.…”

Section: Simulation Approaches For Generating Biologically Relevant A...mentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Simulation Approaches For Generating Biologically Relevant A...mentioning

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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“…It has been suggested that DN-D41 expresses the TCRδ1/TCRβ heterodimer at the cell surface ( 36 ). The full-length TCRδ1, TCRγ, and TCRβ sequences were assembled with stitchR software ( 55 ). The TCRγ sequence used was the example data provided by stitchR.…”

Section: Methodsmentioning

confidence: 99%

The immunopathological landscape of human pre-TCRα deficiency: From rare to common variants

Materna,

Delmonte,

Bosticardo

et al. 2024

Science

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We describe humans with rare biallelic loss-of-function PTCRA variants impairing pre–α T cell receptor (pre-TCRα) expression. Low circulating naive αβ T cell counts at birth persisted over time, with normal memory αβ and high γδ T cell counts. Their TCRα repertoire was biased, which suggests that noncanonical thymic differentiation pathways can rescue αβ T cell development. Only a minority of these individuals were sick, with infection, lymphoproliferation, and/or autoimmunity. We also report that 1 in 4000 individuals from the Middle East and South Asia are homozygous for a common hypomorphic PTCRA variant. They had normal circulating naive αβ T cell counts but high γδ T cell counts. Although residual pre-TCRα expression drove the differentiation of more αβ T cells, autoimmune conditions were more frequent in these patients compared with the general population.

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“…To obtain a full TCRαβ sequence, TCRs without V/J gene information were removed. We used the Stitchr tool to reconstruct the full TCRαβ amino acid sequence from available V(D)J genes and CDR3αβ [29]. TCRαβ sequences that could not be reconstructed were also excluded.…”

Section: Methodsmentioning

confidence: 99%

TCR clustering by contrastive learning on antigen specificity

Pertseva,

Follonier,

Scarcella

et al. 2024

Preprint

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Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pre-trained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.

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Stitchr: stitching coding TCR nucleotide sequences from V/J/CDR3 information

Cited by 26 publications

References 80 publications

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

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

The immunopathological landscape of human pre-TCRα deficiency: From rare to common variants

TCR clustering by contrastive learning on antigen specificity

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