T-Cell receptor constant β germline gene polymorphisms and susceptibility to autoimmune hepatitis

1994 ◽  
Vol 106 (5) ◽  
pp. 1321-1325 ◽  
Author(s):  
Koji Manabe ◽  
Martin L. Hibberd ◽  
Peter T. Donaldson ◽  
James A. Underhill ◽  
Derek G. Doherty ◽  
...  
Neurology ◽  
1992 ◽  
Vol 42 (1) ◽  
pp. 80-80 ◽  
Author(s):  
J. Hillert ◽  
C. Leng ◽  
O. Olerup

2019 ◽  
Vol 132 (15) ◽  
pp. 1796-1801 ◽  
Author(s):  
Hui Ouyang ◽  
Fang Han ◽  
Ze-Chen Zhou ◽  
Qi-Wen Zheng ◽  
Yang-Yang Wang ◽  
...  

2019 ◽  
Author(s):  
Cédric R. Weber ◽  
Rahmad Akbar ◽  
Alexander Yermanos ◽  
Milena Pavlović ◽  
Igor Snapkov ◽  
...  

AbstractSummaryB- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full length variable region immune receptor sequences. ImmuneSIM enables the tuning of the immune receptor features: (i) species and chain type (BCR, TCR, single, paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation, and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis, and machine learning methods for motif detection.AvailabilityThe package is available via https://github.com/GreiffLab/immuneSIM and will also be available at CRAN (submitted). The documentation is hosted at https://[email protected], [email protected]


Hepatology ◽  
1995 ◽  
Vol 22 (1) ◽  
pp. 142-147 ◽  
Author(s):  
Yuji Hoshino ◽  
Nobuyuki Enomoto ◽  
Namiki Izumi ◽  
Masayuki Kurosaki ◽  
Fumiaki Marumo ◽  
...  

Neurology ◽  
1998 ◽  
Vol 51 (2) ◽  
pp. 379-384 ◽  
Author(s):  
J. J. Ma ◽  
M. Nishimura ◽  
H. Mines ◽  
S. Kuroki ◽  
M. Nukina ◽  
...  

Objective: We examined a possible involvement of genetic factors influencing the development of Guillain-Barré syndrome (GBS).Methods: We studied T-cell receptor (TCR), alpha-chain constant (AC), and beta-chain variable (BV) gene polymorphisms using microsatellite markers and serologic HLA class I antigens, HLA-DRB1, and HLA-DQB1 alleles in 81 Japanese patients with GBS and 87 controls.Results: There were no significant differences in these genetic markers between GBS patients and controls. Subgrouping of GBS patients according to recent Campylobacter jejuni infection, the presence of anti-GM1 antibody in the sera, or their combinations also failed to reveal significant associations with these genetic markers. There was, however, a tendency for an increased frequency of HLA-DRB1*0803 in the C. jejuni + GM1 + GBS group, when compared with controls.Conclusions: The data suggest that the roles of TCRAC, T-cell receptor beta-chain variable (TCRBV), HLA class I or class II in the development of GBS are not critical, and further research is necessary to clarify other genes encoded within the HLA region for genetic susceptibility to GBS.


1999 ◽  
Vol 45 (5) ◽  
pp. 595-600 ◽  
Author(s):  
Sucheep Piyasirisilp ◽  
Barbara J. Schmeckpeper ◽  
Dasnayanee Chandanayingyong ◽  
Thiravat Hemachudha ◽  
Diane E. Griffin

2020 ◽  
Vol 36 (11) ◽  
pp. 3594-3596 ◽  
Author(s):  
Cédric R Weber ◽  
Rahmad Akbar ◽  
Alexander Yermanos ◽  
Milena Pavlović ◽  
Igor Snapkov ◽  
...  

Abstract Summary B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection. Availability and implementation The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.


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