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10.1038/s41467-021-25006-7 http://scihub22266oqcxt.onion/10.1038/s41467-021-25006-7 34349111!8339063!34349111     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34349111&cmd=llinks): Failed to open stream: HTTP request failed! 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GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation |pdf=|usr=}}{{34349111}} gab.com Text GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=34349111 #FOAvip 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. Twit Text FOAVip GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=34349111 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34349111 #FOAvip English Wikipedia <ref>{{Cite pmid|34349111}}</ref> GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation #MMPMID34349111 Zhang H; Zhan X; Li B Nat Commun 2021[Aug]; 12 (1): 4699 PMID34349111show ga 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. |*Algorithms[MESH] |COVID-19/diagnosis/immunology[MESH] |Cluster Analysis[MESH] |Complementarity Determining Regions/chemistry/immunology[MESH] |Diagnosis, Differential[MESH] |Epitopes, T-Lymphocyte/chemistry/immunology[MESH] |Humans[MESH] |Multiple Sclerosis/diagnosis/immunology[MESH] |Neoplasms/diagnosis/immunology[MESH] |Receptors, Antigen, T-Cell/chemistry/*classification/immunology[MESH] |SARS-CoV-2[MESH]GIANA allows computationally-efficient TCR clustering and multi-diseas(epmc).pdf DeepDyve Pubget Overpricing