Pub Date : 2025-07-07DOI: 10.1016/j.immuno.2025.100051
Marc Hoffstedt, Hermann Wätzig, Knut Baumann
Various methods, differing in complexity, have been developed to predict T-cell receptor epitopes. tcrdist3, which implements an easy-to-interpret distance-based approach, has demonstrated performance comparable to the best feature-based methods. Here, a new substitution matrix for tcrdist3 is proposed and its performance is compared to various other substitution matrices. Small performance gains were possible; however tcrdist3 was found to perform reliably well with most substitution matrices. Randomly generated substitution matrices were used as a baseline and resulted in good classification results. It was observed that the prediction quality was negatively correlated with the relative standard deviation of the matrix used (i.e. a larger variance of the weights resulted in poorer predictivity). The most important factor of the tcrdist3-distance between two sequences that could be singled out is the number of substitutions. tcrdist3 implicitly considers the number of substitutions and the type of substitution simultaneously. Using substitution matrices with larger variance penalizes certain substitutions more strongly, which blurs the clusters of sequences with the same number of substitutions. Since the number of substitutions was a key predictor, this resulted in decreased prediction performance.
{"title":"Comparison of different substitution matrices for distance based T-cell receptor epitope predictions using tcrdist3","authors":"Marc Hoffstedt, Hermann Wätzig, Knut Baumann","doi":"10.1016/j.immuno.2025.100051","DOIUrl":"10.1016/j.immuno.2025.100051","url":null,"abstract":"<div><div>Various methods, differing in complexity, have been developed to predict T-cell receptor epitopes. tcrdist3, which implements an easy-to-interpret distance-based approach, has demonstrated performance comparable to the best feature-based methods. Here, a new substitution matrix for tcrdist3 is proposed and its performance is compared to various other substitution matrices. Small performance gains were possible; however tcrdist3 was found to perform reliably well with most substitution matrices. Randomly generated substitution matrices were used as a baseline and resulted in good classification results. It was observed that the prediction quality was negatively correlated with the relative standard deviation of the matrix used (i.e. a larger variance of the weights resulted in poorer predictivity). The most important factor of the tcrdist3-distance between two sequences that could be singled out is the number of substitutions. tcrdist3 implicitly considers the number of substitutions and the type of substitution simultaneously. Using substitution matrices with larger variance penalizes certain substitutions more strongly, which blurs the clusters of sequences with the same number of substitutions. Since the number of substitutions was a key predictor, this resulted in decreased prediction performance.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"19 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1016/j.immuno.2025.100055
Marni E. Cueno, Kenichi Imai
Conformational changes in the SARS-CoV-2 spike protein are critical for understanding viral evolution. In this study, we provide comparative structural and electrostatic analyses across variants, revealing both differentiation and reversion patterns not previously described in locked and activated spike conformations. More specifically, we generated SARS2 spike protein models from the various recorded variants between December, 2019 and November 2021, and performed structural superimposition, dendrogram analyses, and electrostatic mapping. We confirmed which locked and activated conformations differed and reversed between the Original spike protein model and subsequent SARS2 variants and subvariants. Additionally, among the spike protein models of subsequent SARS2 variants and subvariants during December, 2019-November, 2021, we likewise established structural variations and reversions among the locked and activated conformations. Moreover, we established the structural relationship and clustering among the locked and activated conformations of the SARS2 spike protein models. Furthermore, we determined the electrostatic potential of all generated SARS2 spike protein models to establish the surface charge distribution. Taken together, we found that certain locked and activated conformations of the Original SARS2 spike protein models exhibited both structural differences and, surprisingly, reversion when compared to subsequent variants and subvariants. Similarly, structural differentiation and reversion were also observed in the locked and activated conformations across the spike protein models. Additionally, we identified distinct structural clusters within the locked and activated conformations, establishing a structural relationship among certain SARS2 spike protein models. Moreover, we found that during spike evolution reorganization of the surface charge distribution occurs during structural differentiation and reversion.
{"title":"Structural insights on the differentiation and reversion of conformational changes in SARS-CoV-2 spike protein models across variants occurring from December, 2019 to November, 2021","authors":"Marni E. Cueno, Kenichi Imai","doi":"10.1016/j.immuno.2025.100055","DOIUrl":"10.1016/j.immuno.2025.100055","url":null,"abstract":"<div><div>Conformational changes in the SARS-CoV-2 spike protein are critical for understanding viral evolution. In this study, we provide comparative structural and electrostatic analyses across variants, revealing both differentiation and reversion patterns not previously described in locked and activated spike conformations. More specifically, we generated SARS2 spike protein models from the various recorded variants between December, 2019 and November 2021, and performed structural superimposition, dendrogram analyses, and electrostatic mapping. We confirmed which locked and activated conformations differed and reversed between the Original spike protein model and subsequent SARS2 variants and subvariants. Additionally, among the spike protein models of subsequent SARS2 variants and subvariants during December, 2019-November, 2021, we likewise established structural variations and reversions among the locked and activated conformations. Moreover, we established the structural relationship and clustering among the locked and activated conformations of the SARS2 spike protein models. Furthermore, we determined the electrostatic potential of all generated SARS2 spike protein models to establish the surface charge distribution. Taken together, we found that certain locked and activated conformations of the Original SARS2 spike protein models exhibited both structural differences and, surprisingly, reversion when compared to subsequent variants and subvariants. Similarly, structural differentiation and reversion were also observed in the locked and activated conformations across the spike protein models. Additionally, we identified distinct structural clusters within the locked and activated conformations, establishing a structural relationship among certain SARS2 spike protein models. Moreover, we found that during spike evolution reorganization of the surface charge distribution occurs during structural differentiation and reversion.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"19 ","pages":"Article 100055"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-27DOI: 10.1016/j.immuno.2025.100053
Andrew M. Collins , Corey T. Watson , Henk-Jan van den Ham , Luc Teyton , Elisa Rosati , Yana Safonova
For over thirty years, an approach to the nomenclatures of human immunoglobulin (IG) and T cell receptor (TR) genes has operated successfully and has been widely supported by the research community. The principles behind the human nomenclatures were then applied to the development of nomenclatures for IG and TR genes in non-human species. More recently, however, genomic sequencing has highlighted the limitations of this historic approach to nomenclature. The sequencing of IG and TR gene loci from multiple individuals and from a number of species has unveiled an extraordinary level of structural variation within the loci of all species that have so far been studied in this way. The designated gene naming authority - the International Union of Immunological Societies (IUIS) IG and TR Nomenclature Sub-Committee - has determined that a more careful approach is required before the genes of any species are officially named. In this opinion piece, we outline the challenges of the IG and TR nomenclatures, hoping to stimulate dialogue within the research community. Such dialogue would help guide the formulation of official policies to determine the appropriate level of knowledge of a locus that should be required before official gene names can be assigned. Strategies are also presented that should allow the unambiguous reporting and discussion of IG and TR gene sequences if their official naming is delayed.
{"title":"Challenges for the immunoglobulin and T cell receptor gene nomenclatures in the modern genomics era","authors":"Andrew M. Collins , Corey T. Watson , Henk-Jan van den Ham , Luc Teyton , Elisa Rosati , Yana Safonova","doi":"10.1016/j.immuno.2025.100053","DOIUrl":"10.1016/j.immuno.2025.100053","url":null,"abstract":"<div><div>For over thirty years, an approach to the nomenclatures of human immunoglobulin (IG) and T cell receptor (TR) genes has operated successfully and has been widely supported by the research community. The principles behind the human nomenclatures were then applied to the development of nomenclatures for IG and TR genes in non-human species. More recently, however, genomic sequencing has highlighted the limitations of this historic approach to nomenclature. The sequencing of IG and TR gene loci from multiple individuals and from a number of species has unveiled an extraordinary level of structural variation within the loci of all species that have so far been studied in this way. The designated gene naming authority - the International Union of Immunological Societies (IUIS) IG and TR Nomenclature Sub-Committee - has determined that a more careful approach is required before the genes of any species are officially named. In this opinion piece, we outline the challenges of the IG and TR nomenclatures, hoping to stimulate dialogue within the research community. Such dialogue would help guide the formulation of official policies to determine the appropriate level of knowledge of a locus that should be required before official gene names can be assigned. Strategies are also presented that should allow the unambiguous reporting and discussion of IG and TR gene sequences if their official naming is delayed.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"19 ","pages":"Article 100053"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-25DOI: 10.1016/j.immuno.2025.100052
Vanessa Mhanna , Gabriel Pires , Grégoire Bohl-Viallefond , Karim El Soufi , Nicolas Tchitchek , David Klatzmann , Adrien Six , Hang P. Pham , Encarnita Mariotti-Ferrandiz
The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRR’s set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.
{"title":"AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis","authors":"Vanessa Mhanna , Gabriel Pires , Grégoire Bohl-Viallefond , Karim El Soufi , Nicolas Tchitchek , David Klatzmann , Adrien Six , Hang P. Pham , Encarnita Mariotti-Ferrandiz","doi":"10.1016/j.immuno.2025.100052","DOIUrl":"10.1016/j.immuno.2025.100052","url":null,"abstract":"<div><div>The analysis of bulk adaptive immune receptor repertoires (AIRR) enables the understanding of immune responses in both normal and pathological conditions. However, the complexity of AIRR calls for advanced, specialized methods to extract meaningful biological insights. These sophisticated approaches often present challenges for researchers with limited bioinformatics expertise, hindering access to comprehensive immune system analysis. To address this challenge, we developed AnalyzAIRR, an AIRR-compliant R package enabling advanced bulk AIRR sequencing data. The tool integrates state-of-the-art statistical and visualization methods applicable at various levels of granularity. It offers a platform for general data exploration, filtering and manipulation, and in-depth cross-comparisons of AIRR datasets, aimed at answering specific biological questions. We illustrate AnalyzAIRR functionalities using a published murine dataset of 18 T-cell receptor repertoires from three diferrent T cell subsets. We first detected and removed a major contaminant in a group of samples, before proceeding with the comparative analysis. Subsequent cross-sample analysis revealed differences in repertoire diversity that aligned with the respective cell phenotypes, and in repertoire convergence among the studied subsets. AnalyzAIRR’s set of analytical metrics is integrated into a Shiny web application and complemented with a tutorial to help users in their analytical strategy, making it user-friendly for biologists with little or no background in bioinformatics.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"19 ","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1016/j.immuno.2025.100050
Pablo Maldonado-Catala , Ram Gouripeddi , Naomi Schlesinger , Julio C. Facelli
Molecular mimicry is one mechanism by which an infectious agent may trigger an autoimmune disease in a human subject and occurs when foreign- and self-peptides contain similar epitopes that activate an autoimmune response in a susceptible individual. Here, we employ a scalable in-silico approach, to identify 861 pairs of known SARS-CoV-2 and autoimmune disease epitopes, out of more than one billion possible pairs. These SARS-CoV-2 epitopes show 1) sequence homology to human autoimmune disorder epitopes, 2) empirical binding data that predict that they bind the same major histocompatibility complex (MHC) molecule and 3) exhibit high empirical immunogenicity. Analysis of these epitope pairs reveals an association between autoimmune disorders, such as type 1 diabetes, autoimmune uveitis, ankylosing spondylitis, and SARS-CoV-2 infection. These associations are consistent with those reported in the literature from the analysis of clinical records.
{"title":"Molecular mimicry impact of the COVID-19 pandemic: Sequence homology between SARS-CoV-2 and autoimmune diseases epitopes","authors":"Pablo Maldonado-Catala , Ram Gouripeddi , Naomi Schlesinger , Julio C. Facelli","doi":"10.1016/j.immuno.2025.100050","DOIUrl":"10.1016/j.immuno.2025.100050","url":null,"abstract":"<div><div>Molecular mimicry is one mechanism by which an infectious agent may trigger an autoimmune disease in a human subject and occurs when foreign- and self-peptides contain similar epitopes that activate an autoimmune response in a susceptible individual. Here, we employ a scalable in-silico approach, to identify 861 pairs of known SARS-CoV-2 and autoimmune disease epitopes, out of more than one billion possible pairs. These SARS-CoV-2 epitopes show 1) sequence homology to human autoimmune disorder epitopes, 2) empirical binding data that predict that they bind the same major histocompatibility complex (MHC) molecule and 3) exhibit high empirical immunogenicity. Analysis of these epitope pairs reveals an association between autoimmune disorders, such as type 1 diabetes, autoimmune uveitis, ankylosing spondylitis, and SARS-CoV-2 infection. These associations are consistent with those reported in the literature from the analysis of clinical records.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"18 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-06DOI: 10.1016/j.immuno.2025.100047
Pratyusha Patidar , Arihant Jain , Tulika Prakash
Autoimmune hemolytic anemia (AIHA) is a chronic autoimmune disease characterized by the self-destruction of red blood cells (RBCs). For investigating the role molecular mimicry in the onset of AIHA manifestations, we identified the microbial epitopes as precipitating factors in the disease etiopathology using an integrated immunoinformatics pipeline which includes sequence homology search between microbial and RBC proteins, followed by B-cell and T-cell epitope prediction. These epitopes were further subjected to a homology search with the human gut microbial proteins. Eight out of the ten analysed infectious agents, including Hepatitis C Virus (HCV), Cytomegalovirus (CMV), Epstein-Barr Virus (EBV), Herpes Simplex Virus (HSV), Human Papillomavirus (HPV), Human Immunodeficiency Virus (HIV), Mycoplasma pneumoniae (MP), and Treponema pallidum (TP), possessed B-cell and T-cell epitopes. Interestingly, EBV, HSV, MP, and TP displayed conformational B-cell epitopes, which overlapped with their linear B-cell epitopes. HLA DRB1_0305 was found to exhibit binding with several bacterial epitopes indicating its predisposing potential to AIHA. Further, we report cross-reactive microbial epitopes against RBC proteins that have been experimentally proven to be associated with AIHA indicating a high possibility of those epitopes causing AIHA. Additionally, many B-cell and T-cell epitopes exhibited exact homologies with various human gut microbial proteins. The functional annotation highlighted the involvement of specialized RBC functions, such as cytoskeleton organization, ammonium homeostasis, signalling transduction, in the underlying disease mechanism. These findings suggest that infection-causing pathogens and gut microbes might have a plausible association with AIHA in the context of molecular mimicry.
{"title":"Deciphering the role of molecular mimicry in the etiopathogenesis of Autoimmune Hemolytic Anemia using an immunoinformatics approach.","authors":"Pratyusha Patidar , Arihant Jain , Tulika Prakash","doi":"10.1016/j.immuno.2025.100047","DOIUrl":"10.1016/j.immuno.2025.100047","url":null,"abstract":"<div><div>Autoimmune hemolytic anemia (AIHA) is a chronic autoimmune disease characterized by the self-destruction of red blood cells (RBCs). For investigating the role molecular mimicry in the onset of AIHA manifestations, we identified the microbial epitopes as precipitating factors in the disease etiopathology using an integrated immunoinformatics pipeline which includes sequence homology search between microbial and RBC proteins, followed by B-cell and T-cell epitope prediction. These epitopes were further subjected to a homology search with the human gut microbial proteins. Eight out of the ten analysed infectious agents, including Hepatitis C Virus (HCV), Cytomegalovirus (CMV), Epstein-Barr Virus (EBV), Herpes Simplex Virus (HSV), Human Papillomavirus (HPV), Human Immunodeficiency Virus (HIV), <em>Mycoplasma pneumoniae</em> (MP), and <em>Treponema pallidum</em> (TP), possessed B-cell and T-cell epitopes. Interestingly, EBV, HSV, MP, and TP displayed conformational B-cell epitopes, which overlapped with their linear B-cell epitopes. HLA DRB1_0305 was found to exhibit binding with several bacterial epitopes indicating its predisposing potential to AIHA. Further, we report cross-reactive microbial epitopes against RBC proteins that have been experimentally proven to be associated with AIHA indicating a high possibility of those epitopes causing AIHA. Additionally, many B-cell and T-cell epitopes exhibited exact homologies with various human gut microbial proteins. The functional annotation highlighted the involvement of specialized RBC functions, such as cytoskeleton organization, ammonium homeostasis, signalling transduction, in the underlying disease mechanism. These findings suggest that infection-causing pathogens and gut microbes might have a plausible association with AIHA in the context of molecular mimicry.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"17 ","pages":"Article 100047"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-06DOI: 10.1016/j.immuno.2025.100048
Ebanja Joseph Ebwanga , Jess Bouhuijzen Wenger , Robert Adamu Shey , Nadine Buys , Rob Lavigne , Stephen Mbigha Ghogomu , Jan Paeshuyse
African swine fever is a severe hemorrhagic swine disease that greatly affects smallholder pig farm productivity in low-income countries as well as some developed countries. Research has shown that the indigenous pigs and wild suids in Africa are either tolerant or resistant to the disease. Also, resistance to disease and favourable production traits are attributed to polymorphism within the major histocompatibility complex (MHC), which is crucial for the vertebrate's adaptive immune response. The polymorphism within the swine leukocyte antigen (SLA) is attributable to host-pathogen co-evolution which results in improved resistance to disease as well as adaptation to diverse environments. While this makes the SLA essential for comparative diversity studies, comparative SLA studies are absent in this context. We undertook SLA-1 and SLA-2 exon-2 comparative genetic diversity study within the locally adapted (local) breed, hybrid (a cross between local and exotic), and the exotic breed of pigs in Cameroon using the polymerase chain reaction sequence-based typing method on 41 animals. Our data analyses provide evidence of positive balancing selection as well as conserved private alleles within the local breeds, the highest expected heterozygosity within the tolerant population while the exotic population had the highest number of haplotypes for both SLA-1 and SLA-2 . The results from this study contribute to our expanding knowledge of SLA genetic diversity while providing the first SLA data for the indigenous and exotic breeds of pigs in Cameroon.
{"title":"Comparative analysis of SLA-1 and SLA-2 genetic diversity in exotic, hybrid, and local pig breeds of Cameroon in relation to adaptive immunity against African swine virus","authors":"Ebanja Joseph Ebwanga , Jess Bouhuijzen Wenger , Robert Adamu Shey , Nadine Buys , Rob Lavigne , Stephen Mbigha Ghogomu , Jan Paeshuyse","doi":"10.1016/j.immuno.2025.100048","DOIUrl":"10.1016/j.immuno.2025.100048","url":null,"abstract":"<div><div>African swine fever is a severe hemorrhagic swine disease that greatly affects smallholder pig farm productivity in low-income countries as well as some developed countries. Research has shown that the indigenous pigs and wild suids in Africa are either tolerant or resistant to the disease. Also, resistance to disease and favourable production traits are attributed to polymorphism within the major histocompatibility complex (MHC), which is crucial for the vertebrate's adaptive immune response. The polymorphism within the swine leukocyte antigen (SLA) is attributable to host-pathogen co-evolution which results in improved resistance to disease as well as adaptation to diverse environments. While this makes the SLA essential for comparative diversity studies, comparative SLA studies are absent in this context. We undertook SLA-1 and SLA-2 exon-2 comparative genetic diversity study within the locally adapted (local) breed, hybrid (a cross between local and exotic), and the exotic breed of pigs in Cameroon using the polymerase chain reaction sequence-based typing method on 41 animals. Our data analyses provide evidence of positive balancing selection as well as conserved private alleles within the local breeds, the highest expected heterozygosity within the tolerant population while the exotic population had the highest number of haplotypes for both SLA-1 and SLA-2 . The results from this study contribute to our expanding knowledge of SLA genetic diversity while providing the first SLA data for the indigenous and exotic breeds of pigs in Cameroon.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"17 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1016/j.immuno.2024.100046
Rodrigo Arcoverde Cerveira , Klara Lenart , Marcel Martin , Matthew James Hinchcliff , Fredrika Hellgren , Kewei Ye , Juliana Assis Geraldo , Taras Kreslavsky , Sebastian Ols , Karin Loré
Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.20) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.
桑格测序仍被广泛应用于各种实验中，通常与流式细胞仪结合使用，对特定细胞群进行索引。然而，现有软件缺乏对原始 Sanger 测序数据进行自动质量控制（QC）并将其与流式细胞仪信息大规模整合的能力。在这里，我们将介绍 scifer，这是一个 R 软件包，目前可在最新发布的 Bioconductor 3.20 中使用，通过对 B 细胞和 T 细胞受体序列的分析，我们展示了它在无缝整合这些类型数据方面的有效性。Scifer 对来自索引分类和免疫受体 Sanger 测序的原始数据进行预处理。它根据长度、Phred 分数和重链互补决定区 3 (HCDR3) 质量等选定参数识别高质量序列。因此，种系分配的质量大大提高，虚假的可变基因突变也减少了。Scifer 是自动化的，可在一小时内处理数千条序列。其输出结果包括质量控制报告、FASTA 文件、汇总表和供人工检查的电图。总之，scifer 是一款用户友好型软件，可加快免疫受体序列的分析速度，具有广泛的适用性。
{"title":"Scifer: An R/Bioconductor package for large-scale integration of Sanger sequencing and flow cytometry data of index-sorted single cells","authors":"Rodrigo Arcoverde Cerveira , Klara Lenart , Marcel Martin , Matthew James Hinchcliff , Fredrika Hellgren , Kewei Ye , Juliana Assis Geraldo , Taras Kreslavsky , Sebastian Ols , Karin Loré","doi":"10.1016/j.immuno.2024.100046","DOIUrl":"10.1016/j.immuno.2024.100046","url":null,"abstract":"<div><div>Sanger sequencing remains widely used in various experimental contexts, often in combination with flow cytometry for indexing specific cell populations. However, existing software lacks the capability to automate quality control (QC) of raw Sanger sequencing data and integrate it with flow cytometry information on a large scale. Here, we introduce scifer, an R package now available in the latest release of Bioconductor (3.20) showcasing its effectiveness in seamlessly integrating these types of data as demonstrated by analyses of B cell and T cell receptor sequences. Scifer preprocesses raw data from index sorts and immune receptor Sanger sequencing. It identifies high-quality sequences based on selected parameters, such as length, Phred scores, and heavy-chain complementarity-determining region 3 (HCDR3) quality. As a result, the quality of germline assignments is significantly increased and spurious variable gene mutations are reduced. Scifer is automated and can process thousands of sequences in less than an hour. Its output provides quality control reports, FASTA files, summarized tables, and electropherograms for manual inspection. In summary, scifer is a user-friendly software that speeds up the analysis of immune receptor repertoire sequences, offering wide applicability.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"16 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-28DOI: 10.1016/j.immuno.2024.100045
Morten Nielsen , Anne Eugster , Mathias Fynbo Jensen , Manisha Goel , Andreas Tiffeau-Mayer , Aurelien Pelissier , Sebastiaan Valkiers , María Rodríguez Martínez , Barthélémy Meynard-Piganeeau , Victor Greiff , Thierry Mora , Aleksandra M. Walczak , Giancarlo Croce , Dana L Moreno , David Gfeller , Pieter Meysman , Justin Barton
Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions.
The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets.
To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions.
The benchmark confirms that current methods achieve reasonable performance in the "seen" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved.
Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.
{"title":"Lessons learned from the IMMREP23 TCR-epitope prediction challenge","authors":"Morten Nielsen , Anne Eugster , Mathias Fynbo Jensen , Manisha Goel , Andreas Tiffeau-Mayer , Aurelien Pelissier , Sebastiaan Valkiers , María Rodríguez Martínez , Barthélémy Meynard-Piganeeau , Victor Greiff , Thierry Mora , Aleksandra M. Walczak , Giancarlo Croce , Dana L Moreno , David Gfeller , Pieter Meysman , Justin Barton","doi":"10.1016/j.immuno.2024.100045","DOIUrl":"10.1016/j.immuno.2024.100045","url":null,"abstract":"<div><div>Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions.</div><div>The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets.</div><div>To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions.</div><div>The benchmark confirms that current methods achieve reasonable performance in the \"seen\" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved.</div><div>Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.</div></div>","PeriodicalId":73343,"journal":{"name":"Immunoinformatics (Amsterdam, Netherlands)","volume":"16 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142426792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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