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M3C A central task ingenomic data analyses for stratified medicine is class discoverywhich is accomplished through clustering. However, an unresolvedproblem with current clustering algorithms is they do not test thenull hypothesis and derive p values. To solve this, we developed anovel hypothesis testing framework that uses consensus clusteringcalled Monte Carlo Consensus Clustering (M3C). M3C use a multi-coreenabled Monte Carlo simulation to generate a distribution ofstability scores for each number of clusters using null datasetswith the same gene-gene correlation structure as the real one.These distributions are used to derive p values and a betadistribution is fitted to the data to cheaply estimate p valuesbeyond the limits of the simulation. M3C improves accuracy, allowsrejection of the null hypothesis, removes systematic bias, and usesp values to make class number decisions. We believe M3C deals witha major pitfall in current automated class discovery tools.
champ.filter() has been totally recoded, now user can do anyfiltering on any data set they want. Merely champ.filter() is focusedto take champ.import() result as input and generate filtered betavalue for future analysis.
Abstract: Simple SummaryAntimicrobial resistance acquired an endemic status in the Pakistan poultry sector. A cross-sectional study was designed to investigate the fecal microbiome and resistome of broiler chickens infected with multidrug-resistant Escherichia coli using next-generation sequencing. Results show the widespread presence of diverse antibiotic resistance genes, virulence-associated genes, plasmid replicon types, and dysbiotic fecal microbial communities. Results indicate that antibiotic resistance altered the fecal microbial community structure of broiler chickens. The use of next-generation sequencing in this study documents a robust and cost-effective approach to study the fecal microbiome and resistome diversities of broiler chickens. AbstractThe study was designed to investigate the fecal microbiome and resistome of broiler chickens infected with multidrug-resistant (MDR) Escherichia coli (E. coli). Fecal samples (n = 410) from broiler chickens were collected from thirteen randomly selected sites of Khyber Pakhtunkhwa and screened for the presence of MDR E. coli. Upon initial screening, thirteen (13) MDR E. coli isolates were then subjected to shotgun metagenome next-generation sequencing (NGS). NGS based resistome analysis identified the multidrug efflux pump system-related genes at the highest prevalence (36%) followed by aminoglycoside (26.1%), tetracycline (15.9%), macrolide-lincosamide-streptogramin (9.6%), beta-lactam (6.6%), rifampin (2%), sulphonamide (1.3%), phenicol (0.91%), vancomycin (0.62%), trimethoprim (0.34%), colistin (0.30%), and quinolone (0.33%). The most abundant virulence-associated genes (VAGs) identified were iroN, iutA, iss, and iucA. NGS based taxonomic profiling at the phylum level revealed the predominance of Proteobacteria (38.9%) followed by Firmicutes (36.4%), Bacteroidetes (15.8%), and Tenericutes (8.9%). Furthermore, pathobionts such as E. coli, Salmonella enterica, Klebsiella pneumoniae, and Shigella flexneri belonging to the family Enterobacteriaceae were predominantly found. This study revealed the widespread presence of MDR genes, diverse VAGs, and a dysbiotic gut in the broiler chickens infected with MDR E. coli of Khyber Pakhtunkhwa for the first time using NGS.Keywords: broiler chickens; fecal microbiota; shotgun metagenome sequencing; dysbiosis; antibiotic resistance genes; multidrug resistance
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