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Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution (±3 months) for the first 1.5 years of life.

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VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.

Brief Bioinform

November 2024

Departamento de Genética del Desarrollo y Fisiología Molecular, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, Morelos 62210, México.

This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus identification often face limitations due to the diversity and rapid evolution of viral genomes. In response, recent efforts have focused on leveraging artificial intelligence (AI) techniques to enhance accuracy and efficiency in virus detection.

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The continuous interaction between phages and their respective hosts has resulted in the evolution of multiple bacterial immune mechanisms. However, the diversity and prevalence of antiviral defense systems in complex communities are still unknown. We therefore investigated the diversity and abundance of viral defense systems in 3,038 high-quality bacterial and archaeal genomes from the rumen.

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Despite the recent surge of viral metagenomic studies, it remains a significant challenge to recover complete virus genomes from metagenomic data. The majority of viral contigs generated from de novo assembly programs are highly fragmented, presenting significant challenges to downstream analysis and inference. To address this issue, we have developed Virseqimprover, a computational pipeline that can extend assembled contigs to complete or nearly complete genomes while maintaining extension quality.

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Background: Mycoplasma pneumoniae (MP) is a common pathogen for respiratory infections in children. Previous studies have reported respiratory tract microbial disturbances associated with MP infection (MPI); however, since the COVID-19 pandemic, respiratory virome data in school-aged children with MPI remains insufficient. This study aims to explore the changes in the respiratory virome caused by MPI after the COVID-19 pandemic to enrich local epidemiological data.

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