Publications by authors named "Victoria Cepeda-Espinoza"

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California.

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Article Synopsis
  • - The Mid-Atlantic Microbiome Meet-up (M) is a collaborative organization that unites academia, government, and industry to enhance microbiome research practices, with a focus on biodefense and infectious disease detection using metagenomics.
  • - The January 2018 meeting highlighted advancements in next-generation sequencing technologies for tracking microbial communities, while also addressing challenges like low sensitivity for certain pathogens and difficulties in quantifying viable organisms.
  • - Participants discussed improving software usability, developing better bioinformatics tools, and establishing data standards to facilitate sharing, all aimed at enhancing the detection and management of biological threats and infectious diseases.
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Metagenomic samples are snapshots of complex ecosystems at work. They comprise hundreds of known and unknown species, contain multiple strain variants and vary greatly within and across environments. Many microbes found in microbial communities are not easily grown in culture making their DNA sequence our only clue into their evolutionary history and biological function.

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Advances in sequencing technologies have led to the increased use of high throughput sequencing in characterizing the microbial communities associated with our bodies and our environment. Critical to the analysis of the resulting data are sequence assembly algorithms able to reconstruct genes and organisms from complex mixtures. Metagenomic assembly involves new computational challenges due to the specific characteristics of the metagenomic data.

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