Biology subjects

Julian, T.

Publications and source records attributed to Julian, T..

4 recordsLinked to original sources

bioRxiv2025

Importance of a heat snap in RT-PCR quantification of rotavirus double-stranded RNA in wastewater

Quantification of copies of double stranded RNA using RT-PCR methods may require denaturation of the double stranded structure using an initial high temperature incubation followed by rapid cooling, herein called "heat snap". Papers in the literature that report rotavirus RNA concentrations in fecal and environmental samples do not consistently report the use of such a "heat snap". In this study, we quantified rotavirus RNA in diverse environmental samples (wastewater solids, wastewater, and drainage samples) using digital RT-PCR methods with and without a heatsnap. Concentrations were higher in samples by a factor of 125 when a heat snap was applied. This was consistent across sample types, and across laboratories and PCR instrumentation. We recommend a heat snap be used when enumerating double stranded RNA from rotavirus and other viruses in environmental samples.

molecular biology↗

bioRxiv2024

Improving inference in wastewater-based epidemiology by modelling the statistical features of digital PCR

Digital polymerase chain reaction (dPCR) is a powerful technique for quantifying gene targets in environmental samples, with various applications such as biodiversity monitoring and wastewater-based epidemiology. However, statistical analyses of environmental dPCR data often assume, explicitly or implicitly, that concentration measurements have a normal or log-normal error structure, which does not reflect the underlying partitioning statistics of dPCR. Using simulations and real-world environmental data, we show that (log-)normality assumptions are violated for dPCR measurements, leading to inaccurate estimates of gene concentrations and underlying biological processes. To enable reliable analyses of environmental dPCR data, we present a dPCR-specific likelihood model that accounts for concentration-dependent measurement noise and non-detects as characteristic of dPCR assays. We demonstrate that this approach overcomes biases in inference from environmental data, such as estimating free-eDNA decay in seawater or pathogen transmission from wastewater monitoring. Our method is implemented in the R packages "dPCRfit" for regression analyses and "EpiSewer" for wastewater surveillance.

bioinformatics↗

bioRxiv2018

nsearch: An open source C++ library for processing and similarity searching of next-generation sequencing data

Advancements in DNA sequencing technologies rapidly change the landscape of modern biology. The novel next-generation sequencing (NGS) applications often have special requirements regarding experimental data processing. Software tools developed and used for novel applications are generally designed for specific use cases and as such may be difficult to adapt to new uses. Simultaneously, software tools designed to be general are often difficult to adapt to special use cases.\n\nHere, we present nsearch, a modern open source C++11 library and command-line tool for biological sequence data processing. nsearch offers commonly used components for handling of biological sequences including paired-end read merging, quality filtering and sequence similarity searching. nsearch can either be embedded natively into other C++ applications or be packaged as a standalone executable.\n\nFunctionality and performance of nsearch is shown using benchmark data created using the Rfam 13 database. Benchmarking against common general purpose tools USEARCH and VSEARCH demonstrates that nsearch delivers performance comparable these state-of-the-art tools.\n\nnsearch is available on GitHub under the permissive BSD-3-clause license: https://github.com/stevschmid/nsearch

bioinformatics↗

bioRxiv2016

Tuning the course of evolution on the biophysical fitness landscape of an RNA virus

Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the \"Evolution Chip\", which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.

evolutionary biology↗