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Julian, T.

Publications and source records attributed to Julian, T..

2 recordsLinked to original sources

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

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