clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

Valkiers, Sebastiaan; Houcke, Max Van; Laukens, Kris; Meysman, Pieter

doi:10.1101/2021.02.22.432291

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Second, those that seek to classify TCRs by identifying similar to known antigen-specific or disease-specific TCR clonotypes. The former algorithms such as TCRdist ( 111 ), GLIPH/GLIPH2 ( 117 , 118 ), ClusTCR ( 119 ), and GIANA ( 108 ) do not need information about T1D-specific epitopes and TCR sequences beforehand, and thus can be used to predict disease-specific TCR clonotypes that are specifically detected in T1D patients but not in non-diabetic subjects. On the other hand, machine learning-based algorithms that assess similarities to known antigen-specific TCR datasets to predict epitopes, such as DeepTCR ( 101 ), DeepCAT ( 107 ), TCRmatch ( 120 ), and TCRAI ( 100 ) need prior information about disease-specific TCR sequences.…”

Section: Use Of Tcr Clonotypes As Surrogates To Quantify Antigen-specific T Cellsmentioning

confidence: 99%

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Nakayama

Michels

2021

Front. Immunol.

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T cell receptors (TCRs) are unique markers that define antigen specificity for a given T cell. With the evolution of sequencing and computational analysis technologies, TCRs are now prime candidates for the development of next-generation non-cell based T cell biomarkers, which provide a surrogate measure to assess the presence of antigen-specific T cells. Type 1 diabetes (T1D), the immune-mediated form of diabetes, is a prototypical organ specific autoimmune disease in which T cells play a pivotal role in targeting pancreatic insulin-producing beta cells. While the disease is now predictable by measuring autoantibodies in the peripheral blood directed to beta cell proteins, there is an urgent need to develop T cell markers that recapitulate T cell activity in the pancreas and can be a measure of disease activity. This review focuses on the potential and challenges of developing TCR biomarkers for T1D. We summarize current knowledge about TCR repertoires and clonotypes specific for T1D and discuss challenges that are unique for autoimmune diabetes. Ultimately, the integration of large TCR datasets produced from individuals with and without T1D along with computational ‘big data’ analysis will facilitate the development of TCRs as potentially powerful biomarkers in the development of T1D.

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Section: Use Of Tcr Clonotypes As Surrogates To Quantify Antigen-specific T Cellsmentioning

confidence: 99%

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Nakayama

Michels

2021

Front. Immunol.

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“…Building a sequence similarity network is computationally expensive. This challenge has been approached by at least two methods that allow the construction of large-scale networks from millions of AIRR sequences [47,51].…”

Section: Similarity Of Airr Sequencesmentioning

confidence: 99%

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis

Marquez

Babrak

Greiff

et al. 2022

Methods in Molecular Biology

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Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.

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GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

Zhang

Zhan

2021

Nat Commun

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Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

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clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

Cited by 9 publications

References 19 publications

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Using the T Cell Receptor as a Biomarker in Type 1 Diabetes

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis

GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

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