De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set

High-throughput sequencing (HTS) is revolutionizing biological research by enabling scientists to quickly and cheaply query variation at a genomic scale. Despite the increasing ease of obtaining such data, using these data effectively still poses notable challenges, especially for those working with organisms without a high-quality reference genome. For every stage of analysis from assembly to annotation to variant discovery researchers have to distinguish technical artefacts from the biological realities of their data before they can make inference. In this work, I explore these challenges by generating a large de novo comparative transcriptomic data set data for a clade of lizards and constructing a pipeline to analyse these data. Then, using a combination of novel metrics and an externally validated variant data set, I test the efficacy of my approach, identify areas of improvement, and propose ways to minimize these errors. I find that with careful data curation, HTS can be a powerful tool for generating genomic data for non-model organisms.