Bibliographic Details
| Title: |
Detection of genomic variations with data from multiple sequencing platforms |
| Authors: |
Yu, Huijing, 余卉菁 |
| Contributors: |
Lam, TW, Luo, R, Ting, HF |
| Publisher Information: |
The University of Hong Kong (Pokfulam, Hong Kong) |
| Publication Year: |
2024 |
| Collection: |
University of Hong Kong: HKU Scholars Hub |
| Subject Terms: |
Genomics - Data processing, Bioinformatics |
| Description: |
The exploration of genomic variations through DNA sequencing data has enriched our comprehension of genetic diversity, unlocking insights into disease association and clinical diagnosis. As sequencing technologies rapidly evolve, bioinformatics methods for detecting genomic variations have progressed, offering accurate variant calls. Nonetheless, challenges persist in detecting variations, contingent upon data availability in clinical and research contexts. Distinct features of sequencing data across sequencing platforms— read length and error profiles—demand tailored approaches. This thesis presents three novel methods (namely, SENSV, Clair3-MP and RepDEL) for different kinds of whole sequencing data to detect genomic variations, even combined data, for accommodating clinical and research objectives. Starting with a comprehension of the specific requirements for each application and an investigation into the limitations of existing methodologies or studies, we carefully devised systematic and innovative approaches to effectively tackle the specified objectives. Subsequently, rigorous experiments were conducted using gold standard evaluation techniques to validate the efficacy and viability of the proposed methods. SENSV is designed to detect structural variations (SVs) using low- depth Oxford Nanopore Technology sequencing data while improving precise breakpoint resolution. SENSV offers precise detection of SVs with low-depth nanopore sequencing data, and these are important in clinical diagnosis of genetic diseases. Clair3-MP is tailored to focus the other types of genomic variations – SNPs and indels, through combining sequencing data from different sequencing platforms. Clair3-MP has elevated the performance for small variant detection in difficult genomic regions. On the other hand, this study also provided insights on the optimal scenarios for variant-calling using multi-platform data through a series of experiments. Clair3-MP offers an accessible platform for research harnessing multi-platform data, ... |
| Document Type: |
doctoral or postdoctoral thesis |
| Language: |
English |
| Relation: |
HKU Theses Online (HKUTO); Yu, H. [余卉菁]. (2023). Detection of genomic variations with data from multiple sequencing platforms. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.; 991044791815203414; https://hub.hku.hk/handle/10722/342883 |
| Availability: |
https://hub.hku.hk/handle/10722/342883 |
| Rights: |
The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Accession Number: |
edsbas.83C5B84 |
| Database: |
BASE |