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<!DOCTYPE html>
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<h1 class="mb-0">Immuno-sequencing Algorithms Laboratory</h1>
<hr class="dna-light">
</br>
<h2 class="font-weight-light mb-0">Bioinformatics · Immunogenetics · Immune repertoire profiling</h2>
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</header>
<!-- Publications Section -->
<section id="publications">
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<h2 class="text-center text-uppercase">Selected publications</h2>
<hr class="pub-dark mb-5">
</br>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen</br><a
href="https://www.nature.com/articles/s43588-024-00653-0" target="_blank">Nature Computational Science 2024</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> <a
href="https://www.nature.com/articles/s43588-024-00654-z" target="_blank">Huo, Jiang & Li in News & Views</a></h4>: A method leverages protein structural data to predict T-cell receptor-peptide interactions for unseen peptide epitopes, which can be particularly useful for applications in cancer immunotherapy, autoimmunity studies, and vaccine design.</p>
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</br>
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<div class="col-lg-6 ml-auto">
<h4>VDJdb in the pandemic era: a compendium of T cell receptors specific for SARS-CoV-2</br><a
href="https://www.nature.com/articles/s41592-022-01578-0" target="_blank">Nature Methods 2022</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> In the current pandemic era, a large majority of recent T cell repertoire profiling and antigen-specificity studies have focused on TCR variants that target the SARS-CoV-2 coronavirus. As a consequence, millions of TCR sequences have now been isolated from donors with COVID-19. To complement these efforts, in the latest release of VDJdb, we incorporated TCR specificity data from various studies of COVID-19.</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T cell receptors</br><a
href="https://www.cell.com/immunity/fulltext/S1074-7613(20)30469-6" target="_blank">Immunity 2020</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> Our results show that while anti-SARS-CoV-2 antibodies can distinguish convalescent patients from healthy donors, the magnitude of T-cell response was more pronounced in healthy donors sampled during COVID-19 pandemic than in donors sampled before the outbreak. This hints at the possibility that some individuals have encountered the virus but were protected by T-cell cross-reactivity observed. A public and diverse T-cell response was observed for two A*02-restricted SARS-CoV-2 epitopes, revealing a set of T-cell receptor motifs displaying germline-encoded features.</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium</br><a
href="https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkz874/5582255" target="_blank">Nucleic Acids Res 2019</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> The increased yield of TCR specificity identification methods and the overall increase in the
number of studies in the field has allowed us to expand the database more than 5-fold. Furthermore, several new analysis methods are
included. For example, batch annotation of TCR repertoire sequencing samples allows for annotating large datasets on-line. Using recently
developed bioinformatic methods for TCR motif mining, we have built a reduced set of high-quality TCR motifs that can be used for both
training TCR specificity predictors and matching against TCRs of interest.</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>Exploring the pre-immune landscape of antigen-specific T cells</br><a
href="https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-018-0577-7" target="_blank">Genome Medicine 2018</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> Our results suggest that the population frequencies of specific T cells are strikingly
non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity
based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to
integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>VDJdb: a curated database of T-cell receptor sequences with known antigen specificity</br><a
href="https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkx760" target="_blank">Nucleic Acids Res 2017</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> The primary goal of VDJdb is to facilitate access to existing information on TCR antigen
specificities, i.e. the ability to recognize known epitopes presented by known major histocompatibility complex (MHC) class I and II
molecules. Our mission is to aggregate TCR specificity information on a continuous basis and establish a curated repository to store these
data in the public domain</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>VDJtools: Unifying Post-analysis of T Cell Receptor Repertoires</br><a
href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004503" target="_blank">PLoS Comp Biol 2015</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> Here we present VDJtools, a software framework that can analyze output of most commonly used
TCR repertoire processing tools and allows applying a diverse set of post-analysis strategies. The main aims of our framework are: To ensure
consistency of post-analysis methods and reproducibility of obtained results; to save the time of bioinformaticians analyzing TCR repertoire
data by providing comprehensive tabular output and open-source API; and to provide a simple enough command line tool so that immunologists
and biologists with little computational background could use it to generate publication-ready results</p>
</div>
</div>
<div class="row">
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>Towards error-free profiling of immune repertoires</br><a href="https://www.nature.com/articles/nmeth.2960" target="_blank">Nature Methods
2014</a></h4>
</div>
<div class="col-lg-6 ml-auto">
<p class="lead"><i class="fas fa-quote-left"> </i> Deep profiling of antibody and T cell–receptor repertoires by means of high-throughput
sequencing has become an attractive approach for adaptive immunity studies, but its power is substantially compromised by the accumulation
of PCR and sequencing errors. Here we report MIGEC (molecular identifier groups–based error correction), a strategy for high-throughput
sequencing data analysis. MIGEC allows for nearly absolute error correction while fully preserving the natural diversity of complex immune
repertoires</p>
</div>
</div>
<div class="row">
</br>
</br>
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4>More publications:</h4>
</br>
<a class="btn btn-xl btn-outline-dark" href="https://www.ncbi.nlm.nih.gov/pubmed/?term=%22Shugay+M%22%5BAuthor%5D" target="_blank">
<i class="fas fa-search-plus mr-2"></i>
PubMed
</a>
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<i class="fas fa-search-plus mr-2"></i>
Google Scholar
</a>
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</div>
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</section>
<!-- Projects Section -->
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<h2 class="text-center text-uppercase text-white">Ongoing projects</h2>
<hr class="proj-light mb-5">
</br>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4 class="text-left text-uppercase text-white"><i class="fas fa-check-circle"></i> T-cell repertoire annotation</h4>
<p class="lead"> Exploring antigen specificities encoded in high-throughput T-cell receptor (TCR) sequencing data using a database of TCR
sequences with known specificity. Discovering immune repertoire biomarkers using statistical approaches to TCR sequence motif inference.
Linking specific TCR repertoire structure to the immunogenicity of cognate antigens.</p>
</div>
<div class="col-lg-6 ml-auto">
<h4 class="text-left text-white"><i class="fas fa-check-circle"></i> MODELING TCR:pMHC COMPLEX</h4>
<p class="lead">In-silico modeling of TCR:peptide:MHC complex structures. Linking structural data and the organization of T-cell repertoire:
CD4/CD8 T-cell differentiation and αβ chain pairing. Building statistical models of TCR:pMHC contacts with an ultimate goal of
developing an efficient method for TCR:pMHC binding prediction.</p>
</div>
</div>
<div class="row">
</br>
</br>
</div>
<div class="row">
<div class="col-lg-6 ml-auto">
<h4 class="text-left text-uppercase text-white"><i class="fas fa-check-circle"></i> Antibodyome analysis</h4>
<p class="lead">Developing fast algorithms for antibody lineage tree inference and somatic hypermutation analysis. Implementing novel approaches
to high-througput antibody sequencing data analysis that focus on the clonal architecture instead of individual sequences/clonotypes.
Exploring differences between B-cell subsets, B-cell memory and the structure of B-cell repertoire in cancer patients.</p>
</div>
<div class="col-lg-6 ml-auto">
<h4 class="text-left text-uppercase text-white"><i class="fas fa-check-circle"></i> Epitope immunogenicity</h4>
<p class="lead">Ranking foreign and self peptides based on their predicted ability to elicit immune response. Searching for physicochemical
characteristics and similarities to self and viral peptidomes that are exploited by the adaptive immune system to act as an efficients
self-vs-nonself classifier. Developing bioinformatic tools for selection of optimal neoantigen targets for cancer immunotherapy.</p>
</div>
</div>
<div class="row">
</br>
</br>
</div>
<div class="row">
<div class="col-lg-2 ml-auto">
</div>
<div class="col-lg-8 ml-auto">
<h4 class="text-left text-uppercase text-white"><i class="fas fa-ambulance"></i> Studying T- and B-cell responses in COVID-19</h4>
<p class="lead">Currently, we are assaying thousands of immune repertoire sequencing datasets coming from convalescent donors, donors that had a history of COVID infection and healthy donors to discover biomarkers that are associated with immunity to COVID-19. We aim at discovering immunogenic SARS-CoV-2 epitopes, as well as T-cell receptor sequences that recognize them. We also study antibody evolution and affinity of various antibodies to distinct SARS-CoV-2 proteins.</p>
</div>
<div class="col-lg-2 ml-auto">
</div>
</div>
</div>
</section>
<!-- Software Grid Section -->
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<h2 class="text-center text-uppercase text-secondary mb-0">Software, databases, tutorials</h2>
<hr class="soft-dark mb-5">
<br>
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<div class="col-md-6 col-lg-4 mb-3">
<a class="d-block mx-auto" href="https://migec.readthedocs.io" target="_blank">
<div class="portfolio-item-caption d-flex position-absolute h-100 w-100">
<div class="portfolio-item-caption-content my-auto w-100 text-center">
<i class="fas fa-search-plus fa-3x"></i>
</div>
</div>
<img class="img-fluid" src="img/migec_splash.png" alt="">
</a>
</div>
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<a class="d-block mx-auto" href="https://vdjtools-doc.readthedocs.io" target="_blank">
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<div class="portfolio-item-caption-content my-auto w-100 text-center">
<i class="fas fa-search-plus fa-3x"></i>
</div>
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<img class="img-fluid" src="img/vdjtools_splash.png" alt="">
</a>
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<a class="portfolio-item d-block mx-auto" href="https://vdjdb.cdr3.net" target="_blank">
<div class="portfolio-item-caption d-flex position-absolute h-100 w-100">
<div class="portfolio-item-caption-content my-auto w-100 text-center">
<i class="fas fa-search-plus fa-3x"></i>
</div>
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<img class="img-fluid" src="img/vdjdb_splash.png" alt="">
</a>
</div>
<div class="col-md-6 col-lg-4 mb-3">
<a class="portfolio-item d-block mx-auto" href="https://vdjviz.cdr3.net" target="_blank">
<div class="portfolio-item-caption d-flex position-absolute h-100 w-100">
<div class="portfolio-item-caption-content my-auto w-100 text-center">
<i class="fas fa-search-plus fa-3x"></i>
</div>
</div>
<img class="img-fluid" src="img/vdjviz_splash.png" alt="">
</a>
</div>
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Mikhail /Mike/ Shugay, Ph.D
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<p class="text-center lead">Principal investigator</p>
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Elizaveta Vlasova, M.Sc
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Daniil Luppov, M.Sc
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Yulia Kremlyakova, M.Sc
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Dmitry Bagaev, Ph.D.
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Vadim Karnaukhov, Ph.D
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Dmitry Shcherbinin, Ph.D
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Anna Obraztsova
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Anastasiya Pivnuk
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Alla Fedorova
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Alexey Eliseev
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Vlad Belousov
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Vasily Tsvetkov
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Mikhail Ignatov
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Pirogov Russian Medical State University / Institute of Bioorganic Chemistry RAS
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National Research Center for Hematology
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Skolkovo Institute of Science and Technology
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Cardiff University
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University of New South Wales
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Sorbonne University
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University of Gothenburg
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University of Oxford
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