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10.1089/cmb.2021.0302 http://scihub22266oqcxt.onion/10.1089/cmb.2021.0302 34698508!8819513!34698508     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34698508&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Comput+Biol 2021 ; 28 (11): 1113-1129 Nephropedia Template TP {{tp|p=34698508|t=2021. From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering |pdf=|usr=}}{{34698508}} gab.com Text From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering JO: J Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34698508 #FOAvip The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets. Twit Text FOAVip From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering ????J Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34698508 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34698508 #FOAvip English Wikipedia <ref>{{Cite pmid|34698508}}</ref> From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Via Clustering #MMPMID34698508 Melnyk A; Mohebbi F; Knyazev S; Sahoo B; Hosseini R; Skums P; Zelikovsky A; Patterson M J Comput Biol 2021[Nov]; 28 (11): 1113-1129 PMID34698508show ga The availability of millions of SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus-2) sequences in public databases such as GISAID (Global Initiative on Sharing All Influenza Data) and EMBL-EBI (European Molecular Biology Laboratory-European Bioinformatics Institute) (the United Kingdom) allows a detailed study of the evolution, genomic diversity, and dynamics of a virus such as never before. Here, we identify novel variants and subtypes of SARS-CoV-2 by clustering sequences in adapting methods originally designed for haplotyping intrahost viral populations. We asses our results using clustering entropy-the first time it has been used in this context. Our clustering approach reaches lower entropies compared with other methods, and we are able to boost this even further through gap filling and Monte Carlo-based entropy minimization. Moreover, our method clearly identifies the well-known Alpha variant in the U.K. and GISAID data sets, and is also able to detect the much less represented (<1% of the sequences) Beta (South Africa), Epsilon (California), and Gamma and Zeta (Brazil) variants in the GISAID data set. Finally, we show that each variant identified has high selective fitness, based on the growth rate of its cluster over time. This demonstrates that our clustering approach is a viable alternative for detecting even rare subtypes in very large data sets. |*Cluster Analysis[MESH] |Brazil[MESH] |Computational Biology/*methods[MESH] |Databases, Genetic[MESH] |Entropy[MESH] |Humans[MESH] |Monte Carlo Method[MESH] |South Africa[MESH] |United Kingdom[MESH]From Alpha to Zeta: Identifying Variants and Subtypes of SARS-CoV-2 Vi(epmc).pdf DeepDyve Pubget Overpricing