Dissertation/ Thesis
Computational methods to bridge genomics data with medical applications
| Τίτλος: | Computational methods to bridge genomics data with medical applications |
|---|---|
| Συγγραφείς: | Su, Junhao, 苏俊豪 |
| Στοιχεία εκδότη: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | Nucleotide sequence - Data processing |
| Περιγραφή: | Recent advancements in third-generation sequencing technologies (TGS) have significantly improved the detection of genetic variants with a broad spectrum. However, the application of the sequencing data from TGS in medical fields is largely hindered by the high error rate from Oxford Nanopore Technologies (ONT) and thus limits the development of tools for sequencing data analysis. This thesis addresses these challenges and provides solutions for application in medical fields. This thesis focuses on developing methods for 1) accurate identification of variants, 2) application in different medical questions, and 3) identifying relationships between genotype and phenotype. Accurate identification of genetic variants is crucial in genome-based genetic studies. Existing approaches for variant calling from family trio data based on ONT data suffer from low detection accuracy due to treating trio variant calling as three independent tasks. To address this problem, Clair3-Trio is developed, which is the first variant caller specifically tailored for family trio data from ONT long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model that inputs trio sequencing information and outputs all predicted variants for the trio in a single model. To further improve accuracy, this thesis introduces the MCVLoss function, which leverages the explicit encoding of Mendelian inheritance. Clair3-Trio has demonstrated large improvements in benchmarks over existing methods, predicting 85% fewer Mendelian inheritance violations. This thesis also establishes the Trio-to-Trio model as the optimal solution to trio variant calling. To facilitate the translation of variant calling into medical applications, this thesis studies two of the most common infectious disease-causing microbes worldwide: Mycobacterium tuberculosis (TB) and Human Immunodeficiency Virus (HIV). The amount of microbes' DNA in metagenomic samples can be low. To address this problem, this thesis applies ONT MinION adaptive sequencing and builds a pipeline named ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Γλώσσα: | English |
| Relation: | HKU Theses Online (HKUTO); Su, J. [苏俊豪]. (2024). Computational methods to bridge genomics data with medical applications. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR.; 991044736607603414; https://hub.hku.hk/handle/10722/335159 |
| Διαθεσιμότητα: | https://hub.hku.hk/handle/10722/335159 |
| 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. |
| Αριθμός Καταχώρησης: | edsbas.7D6113F1 |
| Βάση Δεδομένων: | BASE |
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