Sexually reproducing organisms produce two very different phenotypes (males and females), by differential deployment of essentially the same gene content. This dimorphism provides an excellent model to study how transcriptomes are differentially regulated, which is one of the central problems of biology. The core sex determination pathway of Drosophila is a well described cascade of transcriptional and post-transcriptional regulation, but knowledge of the downstream components is largely incomplete.

High throughput technologies have provided great advances in understanding transcriptome regulation, but limits of the technology have lead to a focus on whole gene expression measurements, rather than post-transcriptional regulation. RNA-Seq experiments, in which transcripts are converted to cDNA and sequenced, allow the resolution and quantification of alternative transcript isoforms, potentially elucidating the post-transcriptional network. However, methods to analyze splicing are underdeveloped, and challenges in transcript assembly and quantification remain unresolved.

This work describes the development of the Splicing Analysis Kit (Spanki) as a fast, open source, suite of tools that uses simulations based on real RNA-Seq data to characterize errors in a given dataset, and user tunable filters that minimize those errors. Spanki quantifies splicing differences in transcripts from the same loci within a sample, as well as between samples by using only those reads that directly assay splicing events (junction spanning reads). Despite the reliance on a fraction of the total data, sequencing depth typically generated in an RNA-Seq experiment is sufficient to identify differentially regulated splicing, and error profiles are superior. I demonstrate that this computational approach outperforms several commonly used approaches in an analysis of sex-differential splicing in Drosophila heads.

Next I examine the effects of disrupting post-transcriptional regulation in Drosophila heads. I apply the Spanki software to analyze RNA-Seq data for mutant lines of two post-transcriptional regulators: Darkener of apricot (Doa) and found in neurons (fne). Doa, a serine-threonine kinase, regulates splicing by phosphorylating SR proteins, vital components of the splicing machinery. Found in neurons (fne) binds to transcripts and is involved in RNA metabolism. I demonstrate sex-differences in response to disruption of post-transcriptional regulation, and hypothesize that they are informative of sex-differentiation pathways.

Finally, I examine the conservation of splicing regulation within the Drosophila lineage. I show that junction based splicing analysis is effective in making interspecific comparisons without the need for complete transcript models. I use these results to demonstrate the conservation of sex-differential splicing across 40 million years of evolution in 15 species in the Drosophila genus.