Browsing by Author "Liu, Yunxi"

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    Crykey: Rapid identification of SARS-CoV-2 cryptic mutations in wastewater
    (Springer Nature, 2024) Liu, Yunxi; Sapoval, Nicolae; Gallego-García, Pilar; Tomás, Laura; Posada, David; Treangen, Todd J.; Stadler, Lauren B.
    Wastewater surveillance for SARS-CoV-2 provides early warnings of emerging variants of concerns and can be used to screen for novel cryptic linked-read mutations, which are co-occurring single nucleotide mutations that are rare, or entirely missing, in existing SARS-CoV-2 databases. While previous approaches have focused on specific regions of the SARS-CoV-2 genome, there is a need for computational tools capable of efficiently tracking cryptic mutations across the entire genome and investigating their potential origin. We present Crykey, a tool for rapidly identifying rare linked-read mutations across the genome of SARS-CoV-2. We evaluated the utility of Crykey on over 3,000 wastewater and over 22,000 clinical samples; our findings are three-fold: i) we identify hundreds of cryptic mutations that cover the entire SARS-CoV-2 genome, ii) we track the presence of these cryptic mutations across multiple wastewater treatment plants and over three years of sampling in Houston, and iii) we find a handful of cryptic mutations in wastewater mirror cryptic mutations in clinical samples and investigate their potential to represent real cryptic lineages. In summary, Crykey enables large-scale detection of cryptic mutations in wastewater that represent potential circulating cryptic lineages, serving as a new computational tool for wastewater surveillance of SARS-CoV-2.
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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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    Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater
    (Springer Nature, 2023) Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G.; Hopkins, Loren; Ensor, Katherine B.; Schneider, Rebecca; Stadler, Lauren B.; Treangen, Todd J.
    As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).
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    Finding Needles in the Haystack: Computational tools for Contaminant Detection and Error Correction in Genomic and Metagenomic Datasets
    (2024-04-17) Liu, Yunxi; Treangen, Todd J
    The scale and complexity of genomic studies have been expanded alongside the volume of sequencing data thanks to the recent development in next-gen and third-gen sequencing technology. However, errors introduced during sample collection, sample preparation, sequencing, and data analysis through computational methods may distort results and contribute to erroneous interpretations. In this work we present a set of studies that explore anomaly detection and error corrections in genomic data, from different points of view. Broadly the topics of the thesis could be grouped into two categories: those related to metagenomic, whereas the projects focus on accurate profiling of a microbiome community in terms of contamination identification, and false positive detection for taxonomic classification; and those related to viral genomics, whereas the projects focus on variant calling with high error rate long read sequencing, and cryptic mutation detection in wastewater for SARS-CoV-2. These methodologies underscore the significance of rare occurrences in high-throughput sequencing procedures, paving the way for advancements in metagenomics and viral genomics.
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    Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens
    (Springer Nature, 2024) Wang, Michael X.; Lou, Esther G.; Sapoval, Nicolae; Kim, Eddie; Kalvapalle, Prashant; Kille, Bryce; Elworth, R. A. Leo; Liu, Yunxi; Fu, Yilei; Stadler, Lauren B.; Treangen, Todd J.; Bioengineering; Civil and Environmental Engineering; Computer Science
    Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.
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    Rescuing low frequency variants within intra-host viral populations directly from Oxford Nanopore sequencing data
    (Springer Nature, 2022) Liu, Yunxi; Kearney, Joshua; Mahmoud, Medhat; Kille, Bryce; Sedlazeck, Fritz J.; Treangen, Todd J.
    Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost. Tracking low frequency intra-host variants provides important insights with respect to elucidating within-host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable computational solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluate Variabel on both synthetic data (SARS-CoV-2) and patient derived datasets (Ebola virus, norovirus, SARS-CoV-2); our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.
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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.