Biology subjects

Elworth, R. A. L.

Publications and source records attributed to Elworth, R. A. L..

6 recordsLinked to original sources

bioRxiv2023

Olivar: fully automated and variant aware primer design for multiplex tiled amplicon sequencing of pathogen genomes

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 2 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 SNPs overlapping with primers and predicted PCR byproducts. We also compared 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 evaluated Olivar on real wastewater samples and found that Olivar had 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 as a web application at https://olivar.rice.edu. Olivar can also be installed locally as a command line tool with Bioconda. Source code, installation guide and usage are available at https://github.com/treangenlab/Olivar.

bioinformatics↗

bioRxiv2021

Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes

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. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs" across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision. Squeegee is open-source and available at: https://gitlab.com/treangenlab/squeegee

bioinformatics↗

bioRxiv2020

Hidden genomic diversity of SARS-CoV-2: implications for qRT-PCR diagnostics and transmission

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 clades that have spread throughout the world. In this study, we investigated over 7,000 SARS-CoV-2 datasets to unveil both intrahost and interhost diversity. Our intrahost and interhost diversity analyses yielded three major observations. First, the mutational profile of SARS-CoV-2 highlights iSNV and SNP similarity, albeit with high variability in C>T 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 small indels fuel 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.

genomics↗

bioRxiv2020

KOMB: Taxonomy-oblivious Characterization of Metagenome Dynamics via K-core Decomposition

Characterizing metagenomic samples via kmer-based, database-dependent taxonomic classification methods has provided crucial insight into underlying host-associated microbiome dynamics. However, novel approaches are needed that are able to track microbial community dynamics within metagenomes to elucidate genome flux in response to perturbations and disease states. Here we describe KOMB, a novel approach for tracking homologous regions within microbiomes. KOMB utilizes K-core graph decomposition on metagenome assembly graphs to identify repetitive and homologous regions to varying degrees of resolution. K-core performs a hierarchical decomposition which partitions the graph into shells containing nodes having degree at least K, called K-shells, yielding O(V + E) complexity compared to exact betweenness centrality complexity of O(V E) found in prior related approaches. We show through rigorous validation on simulated, synthetic, and real metagenomic datasets that KOMB accurately recovers and profiles repetitive and homologous genomic regions across organisms in the sample. KOMB can also identify functionally-rich regions in Human Microbiome Project (HMP) datasets, and can be used to analyze longitudinal data and identify pivotal taxa in fecal microbiota transplantation (FMT) samples. In summary, KOMB represents a novel approach to microbiome characterization that can efficiently identify sequences of interest in metagenomes.

bioinformatics↗

bioRxiv2020

PlasmidHawk: Alignment-based Lab-of-Origin Prediction of Synthetic Plasmids

With advances in synthetic biology and genome engineering comes a heightened awareness of potential misuse related to biosafety concerns. A recent study employed machine learning to identify the lab-of-origin of DNA sequences to help mitigate some of these concerns. Despite their promising results, this deep learning based approach had limited accuracy, is computationally expensive to train, and wasnt able to provide the precise features that were used in its predictions. To address these shortcomings, we have developed PlasmidHawk for lab-of-origin prediction. Compared to a machine learning approach, PlasmidHawk has higher prediction accuracy; PlasmidHawk can successfully predict unknown sequences depositing labs 63% of the time and 80% of the time the correct lab is in the top 10 candidates. In addition, PlasmidHawk can precisely single out the signature sub-sequences that are responsible for the lab-of-origin detection. In summary, PlasmidHawk represents a novel, explainable, accurate tool for lab-of-origin prediction of synthetic plasmid sequences. PlasmidHawk is available at https://gitlab.com/treangenlab/plasmidhawk.git

bioinformatics↗

bioRxiv2019

Diversified RACE Sampling on Data Streams Applied to Metagenomic Sequence Analysis

The rise of whole-genome shotgun sequencing (WGS) has enabled numerous breakthroughs in large-scale comparative genomics research. However, the size of genomic datasets has grown exponentially over the last few years, leading to new challenges for traditional streaming algorithms. Modern petabyte-sized genomic datasets are difficult to process because they are delivered by high-throughput data streams and are difficult to store. As a result, many traditional streaming problems are becoming increasingly relevant. One such problem is the task of constructing a maximally diverse sample over a data stream. In this regime, complex sampling procedures are not possible due to the overwhelming data generation rate. In theory, the best diversity sampling methods are based on a simple greedy algorithm that compares the current sequence with a large pool of sampled sequences and decides whether to accept or reject the sequence. While these methods are elegant and optimal, they are largely confined to the theoretical realm because the greedy procedure is too slow in practice. While there are many methods to identify common elements in data streams efficiently, fast and memory-efficient diversity sampling remains a challenging and fundamental data streaming problem with few satisfactory solutions. In this work, we bridge the gap with RACE sampling, an online algorithm for diversified sampling. Unlike random sampling, which samples uniformly, RACE selectively accepts samples from streams that lead to higher sequence diversity. At the same time, RACE is as computationally efficient as random sampling and avoids pairwise similarity comparisons between sequences. At the heart of RACE lies an efficient lookup array constructed using locality-sensitive hashing (LSH). Our theory indicates that an accept/reject procedure based on LSH lookups is sufficient to obtain a highly diverse subsample. We provide rigorous theoretical guarantees for well-known biodiversity indices and show that RACE can nearly double the Shannon and Simpson indices of a genetic sample in practice, all while using the same resources as random sampling. We also compare RACE against Diginorm and coreset-based diversity sampling methods and find that RACE is faster and more memory efficient. Our algorithm is straightforward to implement, easy to parallelize, and fast enough to keep pace with the overwhelming data generation rates. We expect that as DNA sequence data streams become more mainstream and faster, RACE will become an essential component for many applications.1

bioinformatics↗