BS: Bioinformatics, Rochester Institute of Technology
Chinese hamster ovary (CHO) cells are one of the most preferred cell lines for the production of biotherapeutics. In order to improve the CHO cell line to make safer products more efficiently, a ‘gold-standard’ reference genome for the cell line is necessary. However, CHO cell genomes are unstable and are subject to frequent, spontaneous chromosomal rearrangements. Instead, the Chinese hamster (CH) genome, from which CHO cells were derived, may be an appropriate stable reference genome for CHO cell lines. I have been working on improving the genome assembly of CH by using various assembly methods on CH Illumina and PacBio sequencing data.
In addition, as different assembly algorithms produce different final genome sequences, there is a need for an easy way to choose the assembly of highest quality if multiple algorithms are used. In order to meet this need, I am developing a machine learning approach to examine the accuracy and completeness of mammalian genome assemblies.