Computational Methods for Accelerated Discovery and Characterization of Genes in Emerging Model Organisms.

摘要

Cilia are evolutionarily conserved, complex, microtubule-based structures that protrude from many eukaryotic cells. In humans, cilia can be found on almost all cell types. The effect of abnormal or absent cilia has been established as the common underlying cause of a recently emerging class of genetic diseases collectively referred to as ciliopathies. The function and structure of cilia are conserved across all organisms with cilia. One of the most influential model systems used to study ciliopathies has been the ciliated green alga Chlamydomonas reinhardtii, an organism for which there is a sequenced genome with relatively few experimentally validated whole-gene annotations but in which the ciliogenesis process can be reliably induced. Experimental methods have been successful in identifying a handful of highly specific cilia disease genes in the alga, but high-throughput, automated computational analyses harbor the greatest potential to reveal a more comprehensive ciliopathy disease gene list. However, in order for a genome to be informative for downstream computational analyses, it must first be accurately annotated.;This dissertation focuses on accelerating the accurate annotation of the Chlamydomonas genome using whole-genome and whole-transcriptome methodologies to identify human ciliopathy genes. Towards this end, we first develop a genefinder training method for Chlamydomonas that does not require whole gene annotations and demonstrate that this traning method results in a more accurate genefinder than any other genefinder for this alga. Next, we develop a new automated protein characterization method that facilitates the transfer of information across different protein families by extending simple homology categorization to identify new cilia gene candidates. Finally we perform and analyze high-throughput whole-transcriptome sequencing of Chlamydomonas at various timepoints during ciliogenesis to identify ∼300 novel human ciliopathy gene candidates. Together these three methodologies complement each other and the existing literature to better elucidate a more complete and informative cilia gene catalog.