Tools

Due to divergent codon usage biases across species, codon optimization is often a critical step for the successful expression of exogenous transgenes in nematodes. Platforms for generating DNA sequences codon optimized for the free-living nematode C. elegans are broadly available. However, such tools did not previously exist for non-Caenorhabditis nematodes. We developed the Wild Worm Codon Adapter, a tool for rapid transgene codon optimization for expression in non-Caenorhabditis nematodes. The app includes built-in optimization for parasitic nematodes in the Strongyloides, Nippostrongylus and Brugia genera as well as the predatory nematode Pristionchus pacificus, and C. elegans. The app also supports custom optimization for any species using user-provided optimization rules. Finally, the app supports automated insertion of synthetic, native, or custom introns, as well as the analysis of codon bias in transgene and native sequences. 

Web address: https://bit.ly/WildWormCodon

Project citation: https://pubmed.ncbi.nlm.nih.gov/33914084/

We built a web-based application that provides a user-friendly portal for accessing and analyzing publicly available genomic expression data from four Strongyloides species. This application features a streamlined user interface for exploration of RNA expression levels, on-demand differential gene expression (DGE), and functional enrichment analyses. The Strongyloides RNA-seq Browser prioritizes a user experience that provides access to Strongyloides genomics data and bioinformatics analyses without requiring previous coding experience. 

Web address: https://bit.ly/StrongySeq. 

Project citation: https://pubmed.ncbi.nlm.nih.gov/33823530/

The Caenorhabditis RNA-seq Browser is an open-source Shiny web app that enables on-demand visualization and quantification of bulk RNA-sequencing data for five Caenorhabditis species: C. elegans, C. briggsae, C. brenneri, C. japonica, and C. remanei. The app is designed to allow researchers without previous coding experience to interactively explore publicly available Caenorhabditis RNA-sequencing data. Key app features include the ability to plot gene expression across life stages for user-specified gene sets, and modules for performing differential gene expression analyses. 

Web address: https://bit.ly/CaenSeq

Project citation: https://www.micropublication.org/journals/biology/micropub-biology-001208

As community interest in mechanistic studies of Strongyloides species continues to grow, publicly accessible reference genomes and highly accurate genome annotations are a cornerstone of high-throughput and targeted genomics studies. Genome annotations for multiple Strongyloides species are freely available via the WormBase and WormBase ParaSite online repositories. However, the vast majority of Strongyloides genome annotations are first-pass in-silico gene models generated by an automatic pipeline. The accuracy of these gene models, and thus the predicted Strongyloides proteomes, was generally unknown, and underappreciated by the broader research community. We found that gene model errors are found at high rates across Strongyloides gene families. We therefore developed a standard manual curation workflow for assessing gene annotation quality and generating corrections, as well as a set of recommendations for how it may be used to facilitate community-driven curation of parasitic nematode biodata. 

Project citation: https://pubmed.ncbi.nlm.nih.gov/38008112/

The goal of this project was to generate a Shiny web app for conducing simple power analyses on user-provided data. Most online resources currently require users to provide pre-processed data in the form of individual and group averages. Excel spreadsheets with embedded lookup tables for calculating power analyses require users to be comfortable with several advanced excel features, and assumes familiarity with lookup tables. These requirements may act as a barrier for some researchers. By providing a user-friendly web-based application, we hope to encourage researchers to conduct power analyses on pilot data, when appropriate. 

Web address: https://asbryant.shinyapps.io/Power_Analysis_App/