Katherine A Jensen

Bioinformatics and Computational Biology

Dr. Jessica Schlueter and Dr. Jennifer Weller

Dr. Cynthia Gibas, Dr. Jun-tao Guo, Dr. Anthony Fodor, Dr. Kristen Funk, and Dr. Shan Yan

Computational biology often treats sequence data as a string composed of a restricted character set functioning as multi-layered code. Chemically modified nucleotide structures, as well as folded secondary structures in nucleotide sequences, contain “codes” which have the potential to alter cellular processes and subsequently affect the accuracy of nucleotide sequencing output. To better understand and integrate these levels of information, each study in this project investigated structure-related events with a distinct line of inquiry.
The effect of structure on ONT raw-signal and base-called sequencing read output was first tested on a carefully curated and synthetically constructed collection of structure template DNA sequences. Then, to identify patterns of post-transcriptional modification events within the context of aging, cognition, and Alzheimer’s disease, Illumina RNA-sequencing data of human tissues from cognition-related conditions was assessed using a novel method (patent-pending) which was designed and implemented to capture all variant call-types from filtered data for condition-, tissue-, gene-, and position-related patterns. Finally, to identify and assess SNPs within and between sub-populations across a time series using ONT whole genome sequencing (WGS) from commercially-bred honeybees (Apis mellifera; worker caste), filtered sequencing reads were assembled into “representative” genomes and aligned to these de novo assemblies for variant-calling and haplotype-assignment within and between sub-populations.