Natalia Acevedo Luna - PhD Final Oral Exam - June 20, 2019 10:10 AM

Event
Thursday, June 20, 2019 - 10:10am to 12:10pm
Event Type: 

Bioinformatics and Computational Biology PhD Final Oral Exam

 

Name: Natalia Acevedo Luna
Major Professor: Heike Hofmann
Co-Major Professor: Geetu Tuteja
Department: STAT

Date: June 20th, 2019
Time: 10:10 AM
Location: 1330 ATRB

Title: In-Silico Guided Identification of Ciliogenesis Candidate Genes in a Non-Conventional Animal Model

Abstract:

The annelid Platynereis dumerilii is increasingly used as a model organism for developmental comparative studies. To develop this unconventional model we established PdumBase, an online user interface based on stage specific RNA-seq data that allows genome wide identification of gene families contributing to particular biological processes during early developmental stages. One such important biological process is ciliogenesis, the formation of cilia. However, knowledge of the multiple regulatory mechanisms governing this dynamic process lags behind the functional understanding of these organelles.

To close this gap, we have developed an in-silico guided identification pipeline for genes that contribute to the generation of a multiciliated cell type (MCC). Leveraging differential expression analysis based on wild type vs. experimentally manipulated hyperciliated embryos, our study revealed over 600 statistically significant upregulated genes including a set of core ciliary genes whose biological function was validated through orthologue based annotation methods. To further associate genes that lack any annotation with ciliary activity, we developed DendroShiny, a computational approach  to implicate potentially novel ciliary genes among poorly characterized transcripts. DendroShiny achieves these goals by (1) clustering expression patterns of known genes and (2) using machine learning to determine expression features which allow for the classification of poorly characterized genes. Finally, our approach interactively displays the relationship of the identified clusters and their corresponding expression patterns in order to facilitate downstream analysis of transcriptomic data sets.

Taken together, our approach enables the identification of candidate and potentially novel ciliary genes despite the lack of an annotated genome and sets the ground for the elucidation of regulatory interactions between these candidate genes.

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