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10.12688/f1000research.27214.2 http://scihub22266oqcxt.onion/10.12688/f1000research.27214.2 34316355!8276190!34316355     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       F1000Res 2021 ; 10 (ä): 65 Nephropedia Template TP {{tp|p=34316355|t=2021. iCAT: diagnostic assessment tool of immunological history using high-throughput T-cell receptor sequencing |pdf=|usr=}}{{34316355}} gab.com Text iCAT: diagnostic assessment tool of immunological history using high-throughput T-cell receptor sequencing JO: F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=34316355 #FOAvip The pathogen exposure history of an individual is recorded in their T-cell repertoire and can be accessed through the study of T-cell receptors (TCRs) if the tools to identify them were available. For each T-cell, the TCR loci undergoes genetic rearrangement that creates a unique DNA sequence. In theory these unique sequences can be used as biomarkers for tracking T-cell responses and cataloging immunological history. We developed the immune Cell Analysis Tool (iCAT), an R software package that analyzes TCR sequencing data from exposed (positive) and unexposed (negative) samples to identify TCR sequences statistically associated with positive samples. The presence and absence of associated sequences in samples trains a classifier to diagnose pathogen-specific exposure. We demonstrate the high accuracy of iCAT by testing on three TCR sequencing datasets. First, iCAT successfully diagnosed smallpox vaccinated versus naive samples in an independent cohort of mice with 95% accuracy. Second, iCAT displayed 100% accuracy classifying naive and monkeypox vaccinated mice. Finally, we demonstrate the use of iCAT on human samples before and after exposure to SARS-CoV-2, the virus behind the COVID-19 global pandemic. We were able to correctly classify the exposed samples with perfect accuracy. These experimental results show that iCAT capitalizes on the power of TCR sequencing to simplify infection diagnostics. iCAT provides the option of a graphical, user-friendly interface on top of usual R interface allowing it to reach a wider audience. Twit Text FOAVip iCAT: diagnostic assessment tool of immunological history using high-throughput T-cell receptor sequencing ????F1000Res http://www.kidney.de/pget.php?&tw=1&pmid=34316355 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34316355 #FOAvip English Wikipedia <ref>{{Cite pmid|34316355}}</ref> iCAT: diagnostic assessment tool of immunological history using high-throughput T-cell receptor sequencing #MMPMID34316355 Rajeh A; Wolf K; Schiebout C; Sait N; Kosfeld T; DiPaolo RJ; Ahn TH F1000Res 2021[]; 10 (ä): 65 PMID34316355show ga The pathogen exposure history of an individual is recorded in their T-cell repertoire and can be accessed through the study of T-cell receptors (TCRs) if the tools to identify them were available. For each T-cell, the TCR loci undergoes genetic rearrangement that creates a unique DNA sequence. In theory these unique sequences can be used as biomarkers for tracking T-cell responses and cataloging immunological history. We developed the immune Cell Analysis Tool (iCAT), an R software package that analyzes TCR sequencing data from exposed (positive) and unexposed (negative) samples to identify TCR sequences statistically associated with positive samples. The presence and absence of associated sequences in samples trains a classifier to diagnose pathogen-specific exposure. We demonstrate the high accuracy of iCAT by testing on three TCR sequencing datasets. First, iCAT successfully diagnosed smallpox vaccinated versus naive samples in an independent cohort of mice with 95% accuracy. Second, iCAT displayed 100% accuracy classifying naive and monkeypox vaccinated mice. Finally, we demonstrate the use of iCAT on human samples before and after exposure to SARS-CoV-2, the virus behind the COVID-19 global pandemic. We were able to correctly classify the exposed samples with perfect accuracy. These experimental results show that iCAT capitalizes on the power of TCR sequencing to simplify infection diagnostics. iCAT provides the option of a graphical, user-friendly interface on top of usual R interface allowing it to reach a wider audience. |*COVID-19[MESH] |Animals[MESH] |High-Throughput Nucleotide Sequencing[MESH] |Humans[MESH] |Mice[MESH] |Receptors, Antigen, T-Cell/genetics[MESH] |SARS-CoV-2[MESH]iCAT: diagnostic assessment tool of immunological history using high-t(epmc).pdf DeepDyve Pubget Overpricing