Abstract:
: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.
journal_name
Mol Ecol Resourjournal_title
Molecular ecology resourcesauthors
Singhal Sdoi
10.1111/1755-0998.12077subject
Has Abstractpub_date
2013-05-01 00:00:00pages
403-16issue
3eissn
1755-098Xissn
1755-0998journal_volume
13pub_type
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