De novo genomic analyses for non-model organisms

Scripts were provided for De novo genomic analyses for non-model organisms studies.

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.

• General utility scripts (Singhal): https://github.com/singhal/randomPerlScripts

• Exome capture pipeline (Bi et al, 2012): 
• Transcriptomic pipeline (as was published in Singhal 2013, http://arxiv.org/abs/1211.1737): https://github.com/singhal/transcriptomic
• Exome capture pipeline (Singhal, in prep): https://github.com/singhal/exomeCapture