Dissertation/ Thesis
Detection of genomic variations with data from multiple sequencing platforms
| Τίτλος: | Detection of genomic variations with data from multiple sequencing platforms |
|---|---|
| Συγγραφείς: | Yu, Huijing, 余卉菁 |
| Συνεισφορές: | Lam, TW, Luo, R, Ting, HF |
| Στοιχεία εκδότη: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | Genomics - Data processing, Bioinformatics |
| Περιγραφή: | 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, ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Γλώσσα: | 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 |
| Διαθεσιμότητα: | 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. |
| Αριθμός Καταχώρησης: | edsbas.83C5B84 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hub.hku.hk/handle/10722/342883# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
|---|---|
| Header | DbId: edsbas DbLabel: BASE An: edsbas.83C5B84 RelevancyScore: 867 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 866.983581542969 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Detection of genomic variations with data from multiple sequencing platforms – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Huijing%22">Yu, Huijing</searchLink><br /><searchLink fieldCode="AR" term="%22余卉菁%22">余卉菁</searchLink> – Name: Author Label: Contributors Group: Au Data: Lam, TW<br />Luo, R<br />Ting, HF – Name: Publisher Label: Publisher Information Group: PubInfo Data: The University of Hong Kong (Pokfulam, Hong Kong) – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Hong Kong: HKU Scholars Hub – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Genomics+-+Data+processing%22">Genomics - Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink> – Name: Abstract Label: Description Group: Ab Data: 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, ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: 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 – Name: URL Label: Availability Group: URL Data: https://hub.hku.hk/handle/10722/342883 – Name: Copyright Label: Rights Group: Cpyrght Data: 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. – Name: AN Label: Accession Number Group: ID Data: edsbas.83C5B84 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.83C5B84 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Genomics - Data processing Type: general – SubjectFull: Bioinformatics Type: general Titles: – TitleFull: Detection of genomic variations with data from multiple sequencing platforms Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Huijing – PersonEntity: Name: NameFull: 余卉菁 – PersonEntity: Name: NameFull: Lam, TW – PersonEntity: Name: NameFull: Luo, R – PersonEntity: Name: NameFull: Ting, HF IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |