  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 artifacts 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 dataset data for a clade
of lizards and constructing a pipeline to analyze 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.
