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10.1038/jhg.2016.141 http://scihub22266oqcxt.onion/10.1038/jhg.2016.141 C5334549!5334549!27881843     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Hum+Genet 2017 ; 62 (3): 397-405 Nephropedia Template TP {{tp|p=27881843|t=2017. Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors |pdf=|usr=}}{{27881843}} gab.com Text Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors JO: J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=27881843 #FOAvip Accurate human leukocyte antigen (HLA) genotyping is critical in studies involving the immune system. Several algorithms to estimate HLA genotypes from whole-exome data were developed. We compared the accuracy of seven algorithms, including Optitype, Polysolver and PHLAT, as well as investigated patterns and possible causes of miscalls using 12 clinical samples and 961 individuals from the 1000 Genomes Project. Optitype showed the highest accuracy of 97.2% for HLA class I alleles at the second field resolution, followed by 94.0% in Polysolver and 85.6% in PHLAT. In Optitype, 34 (21.1%) of 161 miscalls were across different serological types, and common miscalls were HLA-A*26:01 to HLA-A*25:01, HLA-B*45:01 to HLA-B*44:15 and HLA-C*08:02 to HLA-C*05:01 with error rates of 4.1%, 10.0% and 4.1%, respectively. In Polysolver, 193 (55.9%) of 345 miscalls occurred across different serological alleles, and a specific pattern of genotyping error from HLA-A*25:01 to HLA-A*26:01 was observed in 93.3% of HLA-A*25:01 carriers, due to dropping of HLA-A*25:01 sequence reads during the extraction process of HLA reads. In PHLAT, 147 (59.8%) of 246 miscalls in HLA-A were due to erroneous assignment of multiple alleles to either HLA-A*01:22 or HLA-A*01:81. These results suggest that careful considerations needed to be taken when using exome-based HLA class I genotyping data and applying these results in clinical settings. Twit Text FOAVip Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors ????J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=27881843 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27881843 #FOAvip English Wikipedia <ref>{{Cite pmid|27881843}}</ref> Comparison of exome-based HLA class I genotyping tools: identification of platform-specific genotyping errors #MMPMID27881843 Kiyotani K; Mai TH; Nakamura Y J Hum Genet 2017[Mar]; 62 (3): 397-405 PMID27881843show ga Accurate human leukocyte antigen (HLA) genotyping is critical in studies involving the immune system. Several algorithms to estimate HLA genotypes from whole-exome data were developed. We compared the accuracy of seven algorithms, including Optitype, Polysolver and PHLAT, as well as investigated patterns and possible causes of miscalls using 12 clinical samples and 961 individuals from the 1000 Genomes Project. Optitype showed the highest accuracy of 97.2% for HLA class I alleles at the second field resolution, followed by 94.0% in Polysolver and 85.6% in PHLAT. In Optitype, 34 (21.1%) of 161 miscalls were across different serological types, and common miscalls were HLA-A*26:01 to HLA-A*25:01, HLA-B*45:01 to HLA-B*44:15 and HLA-C*08:02 to HLA-C*05:01 with error rates of 4.1%, 10.0% and 4.1%, respectively. In Polysolver, 193 (55.9%) of 345 miscalls occurred across different serological alleles, and a specific pattern of genotyping error from HLA-A*25:01 to HLA-A*26:01 was observed in 93.3% of HLA-A*25:01 carriers, due to dropping of HLA-A*25:01 sequence reads during the extraction process of HLA reads. In PHLAT, 147 (59.8%) of 246 miscalls in HLA-A were due to erroneous assignment of multiple alleles to either HLA-A*01:22 or HLA-A*01:81. These results suggest that careful considerations needed to be taken when using exome-based HLA class I genotyping data and applying these results in clinical settings. äComparison of exome-based HLA class I genotyping tools: identification(epmc).pdf DeepDyve Pubget Overpricing