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Mutational Biases in Ascomycota (DSI-SRP)

Posted by on Saturday, August 1, 2020 in College of Arts and Science, Completed Research, DSI-SRP, Natural and Life Sciences, School of Engineering.

This DSI-SRP fellowship funded Qianhui Zheng to work in the laboratory of Professor Antonis Rokas in the Department of Biological Sciences during the summer of 2020. Qianhui anticipates graduating in May 2022 with majors in Computer Science and Biochemistry.

The project funded by this fellowship aimed to understand mutational biases in ascomycota. Mutations are a major source of genetic variability and novelty, providing the fuel for natural selection. Mutations are random with respect to the needs of organisms, but their occurrence in genomes is known to be influenced by certain biases. Understanding these mutational biases is key for understanding how evolution works and for predicting the consequences of increases in mutation rates. Previous studies revealed a mutational bias towards A and T nucleotide bases in certain groups of organisms, such as bacteria. However, the question of what mutational biases exist in fungal genomes remains poorly understood. Zheng’s project examined mutational biases in fungal genomes to: a) test whether fungal genomes experience mutational bias toward AT bases, and b) investigate whether other types of mutational bias. Utilizing the data processing skills Zheng learned from DSI-SRP workshops, he developed a pipeline for collecting and analyzing complex genome data for 30 species, including over 500 strains in Ascomycota. The results (distribution of single nucleotide polymorphisms, transition over transversion ratio, measure of directional mutational bias, etc.) show that while there is no substantial AT mutational bias in most of the species in Ascomycota, Hanseniaspora uvarum and Candida auris do demonstrate strong AT mutational bias. Since Hanseniaspora uvarum is known to have lost an extensive amount of cell-cycle checkpoint and DNA repair genes and Candida auris is an emerging multidrug-resistant yeast pathogen, the results may lead us to new discoveries of implications of specific mutational patterns. Zheng’s DSI-SRP experience served as the basis for his honors thesis research, for which he continue to investigate mutational biases in Ascomycota. In addition, Zheng is developing his pipeline into a software which will be available for the public as a tool to easily examine mutational biases in any species.

In addition to receiving support through a DSI-SRP fellowship, this project was supported and facilitated by the DSI Data Science Team through their regular summer workshops and demo sessions.

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