Dataset Information

Correcting names of bacteria deposited in National Microbial Repositories: an analysed sequence data necessary for taxonomic re-categorization of misclassified bacteria-ONE example, genus Lysinibacillus.

ABSTRACT: A report on 16S rRNA gene sequence re-analysis and digitalization is presented using Lysinibacillus species (one example) deposited in National Microbial Repositories in India. Lysinibacillus species 16S rRNA gene sequences were digitalized to provide quick response (QR) codes, Chaose Game Representation (CGR) and Frequency of Chaose Game Representation (FCGR). GC percentage, phylogenetic analysis, and principal component analysis (PCA) are tools used for the differentiation and reclassification of the strains under investigation. The seven reasons supporting the statements made by us as misclassified Lysinibacillus species deposited in National Microbial Depositories are given in this paper. Based on seven reasons, bacteria deposited in National Microbial Repositories such as Lysinibacillus and many other needs reanalyses for their exact identity. Leaves of identity with type strains of related species shows difference 2 to 8 % suggesting that reclassification is needed to correctly assign species names to the analyzed Lysinibacillus strains available in National Microbial Repositories.

SUBMITTER: Rekadwad BN

PROVIDER: S-EPMC5520958 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correcting names of bacteria deposited in National Microbial Repositories: an analysed sequence data necessary for taxonomic re-categorization of misclassified bacteria-ONE example, genus Lysinibacillus.

Rekadwad Bhagwan N BN Gonzalez Juan M JM

Data in brief 20170705

A report on 16S rRNA gene sequence re-analysis and digitalization is presented using Lysinibacillus species (one example) deposited in National Microbial Repositories in India. Lysinibacillus species 16S rRNA gene sequences were digitalized to provide quick response (QR) codes, Chaose Game Representation (CGR) and Frequency of Chaose Game Representation (FCGR). GC percentage, phylogenetic analysis, and principal component analysis (PCA) are tools used for the differentiation and re ...[more]

PMID: 28761909

Similar Datasets

Project description:Cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO) are common congenital malformations. Numerous epidemiologic studies have shown an increased risk for orofacial clefts among children whose mothers smoked during early pregnancy; however, there is concern that the results of these studies may have been biased because of exposure misclassification. The purpose of this study is to use previous research on the reliability of self-reported cigarette smoking to produce corrected point estimates (and associated credible intervals) of the effect of maternal smoking on children's risk of clefts.We accounted for misclassification using 4 Bayesian models that made different assumptions about the sensitivity and specificity of self-reported maternal smoking data. We used results from previous studies to specify the prior distributions for sensitivity and specificity of reporting and used Markov chain Monte Carlo algorithms to calculate the posterior distribution of the effect of maternal smoking on children's risk for CL/P and CPO.After correcting for potential sources of misclassification in data from the National Birth Defects Prevention Study, we found an increased risk of CL/P among children born to mothers who smoked during early pregnancy (posterior odds ratio [OR] = 1.6, 95% credible interval = 1.1-2.2). The posterior effect of smoking on CPO provided less evidence of effect (posterior OR = 1.1, 95% credible interval = 0.7-1.7).Our results lend some credibility to the hypothesis that periconceptional maternal smoking increases the risk of a child being born with CL/P. The results concerning CPO provide no overall evidence of effect, although the estimates were relatively imprecise. We suggest that future research should emphasize validity studies, especially those of differential reporting, rather than replicating existing analyses of the relationship between maternal smoking and clefts. We discuss how our approach is also applicable to evaluating misclassification in a wide range of exposure-outcome scenarios.

Dataset Information

Correcting names of bacteria deposited in National Microbial Repositories: an analysed sequence data necessary for taxonomic re-categorization of misclassified bacteria-ONE example, genus Lysinibacillus.

Publications

Correcting names of bacteria deposited in National Microbial Repositories: an analysed sequence data necessary for taxonomic re-categorization of misclassified bacteria-ONE example, genus <i>Lysinibacillus</i>.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets