Publications by authors named "Oshma Chakoory"

The discovery that the lung harbors a diverse microbiome, as revealed by next-generation sequencing, has significantly altered our understanding of respiratory health and disease. Despite the association between the lung microbiota and disease, the nature of their relationship remains poorly understood, and culture isolation of these microorganisms could help to determine their role in lung physiology. Current procedures for processing samples from the lower respiratory tract have been shown to affect the viability of microorganisms, so it is crucial to develop new methods to improve their survival.

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In recent decades, preterm birth (PTB) has become a significant research focus in the healthcare field, as it is a leading cause of neonatal mortality worldwide. Using five independent study cohorts including 1290 vaginal samples from 561 pregnant women who delivered at term (n = 1029) or prematurely (n = 261), we analysed vaginal metagenomics data for precise microbiome structure characterization. Then, a deep neural network (DNN) was trained to predict term birth (TB) and PTB with an accuracy of 84.

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Next-generation sequencing technologies have impressively unlocked capacities to depict the complexity of microbial communities. Microbial community structure is for now routinely monitored by sequencing of 16S rRNA gene, a phylogenetic marker almost conserved among bacteria and archaea. Nevertheless, amplicon sequencing, the most popular used approach, suffers from several biases impacting the picture of microbial communities.

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Metagenomic classifiers are widely used for the taxonomic profiling of metagenomics data and estimation of taxa relative abundance. Small subunit rRNA genes are a gold standard for phylogenetic resolution of microbiota, although the power of this marker comes down to its use as full-length. We aimed at identifying the tools that can efficiently lead to taxonomic resolution down to the species level.

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