The World Health Organization characterized COVID-19 as a pandemic in March 2020, the second pandemic of the twenty-first century. Expanding virus populations, such as that of SARS-CoV-2, accumulate a number of narrowly shared polymorphisms, imposing a confounding effect on traditional clustering methods. In this context, approaches that reduce the complexity of the sequence space occupied by the SARS-CoV-2 population are necessary for robust clustering. Here, we propose subdividing the global SARS-CoV-2 population into six well-defined subtypes and 10 poorly represented genotypes named tentative subtypes by focusing on the widely shared polymorphisms in nonstructural (nsp3, nsp4, nsp6, nsp12, nsp13 and nsp14) cistrons and structural (spike and nucleocapsid) and accessory (ORF8) genes. The six subtypes and the additional genotypes showed amino acid replacements that might have phenotypic implications. Notably, three mutations (one of them in the Spike protein) were responsible for the geographical segregation of subtypes. We hypothesize that the virus subtypes detected in this study are records of the early stages of SARS-CoV-2 diversification that were randomly sampled to compose the virus populations around the world. The genetic structure determined for the SARS-CoV-2 population provides substantial guidelines for maximizing the effectiveness of trials for testing candidate vaccines or drugs.
16The World Health Organization characterized the COVID-19 as a pandemic in March 2020, the second 17 pandemic of the 21 st century. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 18 positive-stranded RNA betacoronavirus of the family Coronaviridae. Expanding virus populations, as 19 that of SARS-CoV-2, accumulate a number of narrowly shared polymorphisms imposing a 20 confounding effect on traditional clustering methods. In this context, approaches that reduce the 21 complexity of the sequence space occupied by the SARS-CoV-2 population are necessary for a robust 22 clustering. Here, we proposed the subdivision of the global SARS-CoV-2 population into sixteen well-23 defined subtypes by focusing on the widely shared polymorphisms in nonstructural (nsp3, nsp4, nsp6, 24 nsp12, nsp13 and nsp14) cistrons, structural (spike and nucleocapsid) and accessory (ORF8) genes. 25Six virus subtypes were predominant in the population, but all sixteen showed amino acid 26 replacements which might have phenotypic implications. We hypothesize that the virus subtypes 27 detected in this study are records of the early stages of the SARS-CoV-2 diversification that were 28 randomly sampled to compose the virus populations around the world, a typical founder effect. The 29 genetic structure determined for the SARS-CoV-2 population provides substantial guidelines for 30 maximizing the effectiveness of trials for testing the candidate vaccines or drugs. Main 32In December 2019, a local pneumonia outbreak of initially unknown etiology was detected in 33 Wuhan (Hubei, China) and quickly determined to be caused by a novel coronavirus 1 , named Severe 34 acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 2 and the disease as COVID-19 3 . SARS- 35CoV-2 is classified in the family Coronaviridae, genus Betacoronavirus, which comprises enveloped, 36 positive stranded RNA viruses of vertebrates 2 . Two-thirds of SARS-CoVs genome is covered by the 37 ORF1ab, that encodes a large polypeptide which is cleaved into 16 nonstructural proteins (NSPs) 38 involved in replication-transcription in vesicles from endoplasmic reticulum (ER)-derived 39 membranes 4,5 . The last third of the virus genome encodes four essential structural proteins: spike (S), 40 envelope (E), membrane (M), nucleocapsid (N) and several accessory proteins that interfere with the 41 host innate immune response 6 . 42Populations of RNA viruses evolve rapidly due to their large population sizes, short generation 43 times, and high mutation rates, this latter being a consequence of the RNA-dependent RNA 44 polymerase (RdRP) which lacks the proofreading activity 7 . In fact, virus populations are composed of 45 a broad spectrum of closely related genetic variants resembling one or more master sequences [8][9][10] . 46 Mutation rates inferred for SARS-CoVs are considered moderate 11,12 due to the independent 47 proofreading activity 13 . However, the large SARS-CoV genomes (from 27 to 31 kb) 14 provide to them 48 the ability to explore the sequence spa...
The objective of this work was to analyze the genetic diversity using conventional methods and artificial neural networks among 12 colored fiber cotton genotypes, using technological characteristics of the fiber and productivity in terms of cottonseed and cotton fiber yield. The experiment was conducted in an experimental area located at Fazenda Capim Branco, belonging to the Federal University of Uberlândia, in the city of Uberlândia, Minas Gerais. Twelve genotypes of colored fiber cotton were evaluated, 10 from the Cotton Genetic Improvement Program (PROMALG): UFUJP - 01, UFUJP - 02, UFUJP - 05, UFUJP - 08, UFUJP - 09, UFUJP - 10, UFUJP - 11, UFUJP - 13, UFUJP - 16, UFUJP - 17 and two commercial cultivars: BRS Rubi (RC) and BRS Topázio (TC). The experimental design used was complete randomized block (CRB) with three replications. The following evaluations were carried out at full maturation: yield of cottonseed (kg ha-1) and the technological characteristics, which include, fiber length, micronaire, maturation, length uniformity, short fiber index, elongation and strength, using the HVI (High volume instrument) device. Genetic dissimilarity was measured using the generalized Mahalanobis distance and after obtaining the dissimilarity matrix, the genotypes were grouped using a hierarchical clustering method (UPGMA). A discriminant analysis and the Kohonen Self-Organizing Map (SOM) by Artificial Neural Networks (ANN’s) were performed through computational intelligence. SOM was able to detect differences and organize the similarities between accesses in a more coherent way, forming a larger number of groups, when compared to the method that uses the Mahalanobis matrix. It was also more accurate than the discriminant analysis, since it made it possible to differentiate groups more coherently when comparing their phenotypic behavior. The methods that use computational intelligence proved to be more efficient in detecting similarity, with Kohonen's Self-Organizing Map being the most adequate to classify and group cotton genotypes.
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