Data from: 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.