Computer Science

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Browsing Computer Science by Author "Aagaard, Kjersti M."

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    De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee
    (Springer Nature, 2022) Liu, Yunxi; Elworth, R. A. Leo; Jochum, Michael D.; Aagaard, Kjersti M.; Treangen, Todd J.
    Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.
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    SARS-CoV-2 genomic diversity and the implications for qRT-PCR diagnostics and transmission
    (Cold Spring Harbor Laboratory Press, 2021) Sapoval, Nicolae; Mahmoud, Medhat; Jochum, Michael D.; Liu, Yunxi; Elworth, R. A. Leo; Wang, Qi; Albin, Dreycey; Ogilvie, Huw A.; Lee, Michael D.; Villapol, Sonia; Hernandez, Kyle M.; Berry, Irina Maljkovic; Foox, Jonathan; Beheshti, Afshin; Ternus, Krista; Aagaard, Kjersti M.; Posada, David; Mason, Christopher E.; Sedlazeck, Fritz J.; Treangen, Todd J.
    The COVID-19 pandemic has sparked an urgent need to uncover the underlying biology of this devastating disease. Though RNA viruses mutate more rapidly than DNA viruses, there are a relatively small number of single nucleotide polymorphisms (SNPs) that differentiate the main SARS-CoV-2 lineages that have spread throughout the world. In this study, we investigated 129 RNA-seq data sets and 6928 consensus genomes to contrast the intra-host and inter-host diversity of SARS-CoV-2. Our analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights intra-host single nucleotide variant (iSNV) and SNP similarity, albeit with differences in C > U changes. Second, iSNV and SNP patterns in SARS-CoV-2 are more similar to MERS-CoV than SARS-CoV-1. Third, a significant fraction of insertions and deletions contribute to the genetic diversity of SARS-CoV-2. Altogether, our findings provide insight into SARS-CoV-2 genomic diversity, inform the design of detection tests, and highlight the potential of iSNVs for tracking the transmission of SARS-CoV-2.