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Duedu, K. O.

Publications and source records attributed to Duedu, K. O..

2 recordsLinked to original sources

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 4,471,579 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of June 2024, viewable at https://viridian.taxonium.org. Each genome was constructed using a novel assembly tool called Viridian (https://github.com/iqbal-lab-org/viridian), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

bioinformatics↗

Comparative assessment of methods for determining adiposity and a model for obesity index

BackgroundObesity is increasingly becoming a pandemic considering the many risks it pose to other disease conditions. Obesity is largely a measure of adiposity, however, adiposity is not centralized in the human body. This makes it difficult for any single method to adequately represent obesity and by extension the risks specific areas of adipose accumulation pose to specific disease conditions.\n\nSubjects/MethodsWe evaluated the prevalence of obesity in a cohort of Ghanaian women using the body mass index (BMI) and further sought to evaluate how it compares to other methods of estimating adiposity and the suitability of any particular methods representing obesity in general. We used anthropometry and bioimpedance derived measures of adiposity and derived other indices such as the abdominal volume index (AVI), body adiposity index (BAI) and conicity index (CI).\n\nResultsWaist and hip circumference, body fat (%BF) and visceral fat (VF) were positively correlated to BMI whereas skeletal muscle mass was negatively correlated. Physical activity indices did not show any significant correlation with BMI. Prevalence of obesity was 16% and 31% using BMI and %BF respectively. Receiver operating characteristic analysis showed that whereas BMI is effective in predicting underweight, normal weight and obesity it was a poor predictor of overweight.\n\nConclusionsThere was also no single measure that could adequately predict obesity as an accumulation fat. Hence, we developed and propose a model as a factor of BAI, %BF, VF and BMI. This model should correctly represent a persons adiposity status and hence should be evaluated in large cohort studies.

physiology↗