Title:

The Effect of Transcriptome Assembly Quality on Phylogenetic Relationships

The processing and quality of genetic data is vital for studying the genetic code of organisms. Researchers often move on to downstream data analysis and overlook the quality of their upstream transcriptomic assemblies. This study examined if the quality of the assembled genetic data affected the resulting inferences about the relatedness between species. It also examined which quality metrics are the best for assessing the quality of assemblies with the goal of making a phylogeny. I assembled sequence data from vertebrates into transcriptomes using the multiple assembler, multiple kmer approach built into the Oyster River Protocol. I took the best and worst assemblies from each organism generated from this process and compiled each into datasets, one with high- and one with low-quality data. I did this for three different quality metrics and created phylogenetic trees for each dataset. I then compared the trees made from the high- and low-quality datasets to an accepted vertebrate tree to assess the differences in the datasets. I show that poor assembly quality has a significant effect on the amount of downstream data and the quality of phylogenetic results. I also developed an understanding of the strengths and weaknesses of each assembly software and assembly quality metric.

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