title: De novo transcriptomic analyses for non-model organisms: an evaluation of methods across a multi-species data set.
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.
authors: Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, CA 94720-3160, USA. singhal@berkeley.edu
authors: Sonal
authors: S
authors: Singhal
mesh_terms: D000818:Animals; D001483:Base Sequence; D020869:Gene Expression Profiling / Q000379:methods*; D015723:Gene Library; D057166:High-Throughput Screening Assays / Q000379:methods*; D008116:Lizards / Q000235:genetics*; D058977:Molecular Sequence Annotation / Q000379:methods; D008969:Molecular Sequence Data; D012107:Research Design; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods; D013045:Species Specificity