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A Computational Approach for Diagnostic Long-Read Genome Sequencing

Kautto, Esko Antero
http://orcid.org/0000-0001-8675-8506
http://rave.ohiolink.edu/etdc/view?acc_num=osu1650201257142888

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
Our understanding of the human genome has greatly expanded since the completion of the Human Genome Project. Many large-scale landmark studies have since looked at the role genetic alterations play in the predisposition to disease and identified countless disease-causing mutations. While most of genomics-based research has been made possible through the commoditization of massively parallel next-generation sequencing, recent advances in sequencing technologies have allowed long-read single-molecule sequencing to further characterize and identify genetic alterations that were previously challenging to detect through conventional sequencing. In this research, we have used accurate long-read sequencing from Pacific Biosciences to study cancer and non-cancer samples alike to identify and characterize disease-associated genetic alterations. The work has involved the development of computational methods for stream-lining analysis of such data to provide high-confidence structural variant calls. The analysis pipeline and tools have been used to accurately identify causative mutations in pediatric cancer cases, discover an internal tandem duplication in the HOXD13 gene that caused syndactyly in two unrelated families, and to expand the role that activating FGFR1 mutations may play in closed spinal dysraphism.
James Blachly (Advisor)
Richard Wilson (Advisor)
128 p.
Bioinformatics

Recommended Citations

Citations

  • Kautto, E. A. (2022). A Computational Approach for Diagnostic Long-Read Genome Sequencing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650201257142888

    APA Style (7th edition)

  • Kautto, Esko. A Computational Approach for Diagnostic Long-Read Genome Sequencing. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1650201257142888.

    MLA Style (8th edition)

  • Kautto, Esko. "A Computational Approach for Diagnostic Long-Read Genome Sequencing." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1650201257142888

    Chicago Manual of Style (17th edition)

osu1650201257142888
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This open access ETD is published by The Ohio State University and OhioLINK.