Dissertation/ Thesis
Generalized pattern matching applied to genetic analysis. ; 通用性模式匹配在基因序列分析中的應用 ; CUHK electronic theses & dissertations collection ; Digital dissertation consortium ; Tong yong xing mo shi pi pei zai ji yin xu lie fen xi zhong de ying yong
| Τίτλος: | Generalized pattern matching applied to genetic analysis. ; 通用性模式匹配在基因序列分析中的應用 ; CUHK electronic theses & dissertations collection ; Digital dissertation consortium ; Tong yong xing mo shi pi pei zai ji yin xu lie fen xi zhong de ying yong |
|---|---|
| Συνεισφορές: | Ni, Bing., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Έτος έκδοσης: | 2011 |
| Συλλογή: | The Chinese University of Hong Kong: CUHK Digital Repository / 香港中文大學數碼典藏 |
| Θεματικοί όροι: | Combinatorial analysis, Computational biology, Computer algorithms, DNA--Analysis--Data processing, Genetics--Methodology, Matching theory, Proteins--Analysis--Data processing, Computational Biology--methods, Sequence Analysis, DNA, Protein |
| Περιγραφή: | Approximate pattern matching problem is, given a reference sequence T, a pattern (query) Q, and a maximum allowed error e, to find all the substrings in the reference, such that the edit distance between the substrings and the pattern is smaller than or equal to the maximum allowed error. Though it is a well-studied problem in Computer Science, it gains a resurrection in Bioinformatics in recent years, largely due to the emergence of the next-generation high-throughput sequencing technologies. This thesis contributes in a novel generalized pattern matching framework, and applies it to solve pattern matching problems in general and alternative splicing detection (AS) in particular. AS is to map a large amount of next-generation sequencing short reads data to a reference human genome, which is the first and an important step in analyzing the sequenced data for further Biological analysis. The four parts of my research are as follows. ; In the first part of my research work, we propose a novel deterministic pattern matching algorithm which applies Agrep, a well-known bit-parallel matching algorithm, to a truncated suffix array. Due to the linear cost of Agrep, the cost of our approach is linear to the number of characters processed in the truncated suffix array. We analyze the matching cost theoretically, and .obtain empirical costs from experiments. We carry out experiments using both synthetic and real DNA sequence data (queries) and search them in Chromosome-X of a reference human genome. The experimental results show that our approach achieves a speed-up of several magnitudes over standard Agrep algorithm. ; In the fourth part, we focus on the seeding strategies for alternative splicing detection. We review the history of seeding-and-extending (SAE), and assess both theoretically and empirically the seeding strategies adopted in existing splicing detection tools, including Bowtie's heuristic and ABMapper's exact seedings, against the novel complementary quad-seeding strategy we proposed and the corresponding ... |
| Τύπος εγγράφου: | thesis |
| Περιγραφή αρχείου: | electronic resource; microform; microfiche; 1 online resource (xxi, 161 leaves : ill. (some col.)) |
| Γλώσσα: | English Chinese |
| ISBN: | 978-1-267-19937-9 1-267-19937-7 |
| Relation: | cuhk:344817; http://library.cuhk.edu.hk/record=b6075184 |
| Διαθεσιμότητα: | http://library.cuhk.edu.hk/record=b6075184 https://repository.lib.cuhk.edu.hk/en/item/cuhk-344817 |
| Rights: | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
| Αριθμός Καταχώρησης: | edsbas.AF00F1D2 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: http://library.cuhk.edu.hk/record=b6075184# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
|---|---|
| Header | DbId: edsbas DbLabel: BASE An: edsbas.AF00F1D2 RelevancyScore: 765 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 764.654296875 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Generalized pattern matching applied to genetic analysis. ; 通用性模式匹配在基因序列分析中的應用 ; CUHK electronic theses & dissertations collection ; Digital dissertation consortium ; Tong yong xing mo shi pi pei zai ji yin xu lie fen xi zhong de ying yong – Name: Author Label: Contributors Group: Au Data: Ni, Bing.<br />Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. – Name: DatePubCY Label: Publication Year Group: Date Data: 2011 – Name: Subset Label: Collection Group: HoldingsInfo Data: The Chinese University of Hong Kong: CUHK Digital Repository / 香港中文大學數碼典藏 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Combinatorial+analysis%22">Combinatorial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+biology%22">Computational biology</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+algorithms%22">Computer algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22DNA--Analysis--Data+processing%22">DNA--Analysis--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics--Methodology%22">Genetics--Methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Matching+theory%22">Matching theory</searchLink><br /><searchLink fieldCode="DE" term="%22Proteins--Analysis--Data+processing%22">Proteins--Analysis--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+Biology--methods%22">Computational Biology--methods</searchLink><br /><searchLink fieldCode="DE" term="%22Sequence+Analysis%22">Sequence Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22DNA%22">DNA</searchLink><br /><searchLink fieldCode="DE" term="%22Protein%22">Protein</searchLink> – Name: Abstract Label: Description Group: Ab Data: Approximate pattern matching problem is, given a reference sequence T, a pattern (query) Q, and a maximum allowed error e, to find all the substrings in the reference, such that the edit distance between the substrings and the pattern is smaller than or equal to the maximum allowed error. Though it is a well-studied problem in Computer Science, it gains a resurrection in Bioinformatics in recent years, largely due to the emergence of the next-generation high-throughput sequencing technologies. This thesis contributes in a novel generalized pattern matching framework, and applies it to solve pattern matching problems in general and alternative splicing detection (AS) in particular. AS is to map a large amount of next-generation sequencing short reads data to a reference human genome, which is the first and an important step in analyzing the sequenced data for further Biological analysis. The four parts of my research are as follows. ; In the first part of my research work, we propose a novel deterministic pattern matching algorithm which applies Agrep, a well-known bit-parallel matching algorithm, to a truncated suffix array. Due to the linear cost of Agrep, the cost of our approach is linear to the number of characters processed in the truncated suffix array. We analyze the matching cost theoretically, and .obtain empirical costs from experiments. We carry out experiments using both synthetic and real DNA sequence data (queries) and search them in Chromosome-X of a reference human genome. The experimental results show that our approach achieves a speed-up of several magnitudes over standard Agrep algorithm. ; In the fourth part, we focus on the seeding strategies for alternative splicing detection. We review the history of seeding-and-extending (SAE), and assess both theoretically and empirically the seeding strategies adopted in existing splicing detection tools, including Bowtie's heuristic and ABMapper's exact seedings, against the novel complementary quad-seeding strategy we proposed and the corresponding ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: electronic resource; microform; microfiche; 1 online resource (xxi, 161 leaves : ill. (some col.)) – Name: Language Label: Language Group: Lang Data: English<br />Chinese – Name: ISBN Label: ISBN Group: ISBN Data: 978-1-267-19937-9<br />1-267-19937-7 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: cuhk:344817; http://library.cuhk.edu.hk/record=b6075184 – Name: URL Label: Availability Group: URL Data: http://library.cuhk.edu.hk/record=b6075184<br />https://repository.lib.cuhk.edu.hk/en/item/cuhk-344817 – Name: Copyright Label: Rights Group: Cpyrght Data: Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) – Name: AN Label: Accession Number Group: ID Data: edsbas.AF00F1D2 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.AF00F1D2 |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English – Text: Chinese Subjects: – SubjectFull: Combinatorial analysis Type: general – SubjectFull: Computational biology Type: general – SubjectFull: Computer algorithms Type: general – SubjectFull: DNA--Analysis--Data processing Type: general – SubjectFull: Genetics--Methodology Type: general – SubjectFull: Matching theory Type: general – SubjectFull: Proteins--Analysis--Data processing Type: general – SubjectFull: Computational Biology--methods Type: general – SubjectFull: Sequence Analysis Type: general – SubjectFull: DNA Type: general – SubjectFull: Protein Type: general Titles: – TitleFull: Generalized pattern matching applied to genetic analysis. ; 通用性模式匹配在基因序列分析中的應用 ; CUHK electronic theses & dissertations collection ; Digital dissertation consortium ; Tong yong xing mo shi pi pei zai ji yin xu lie fen xi zhong de ying yong Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ni, Bing. – PersonEntity: Name: NameFull: Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2011 Identifiers: – Type: isbn-print Value: 9781267199379 – Type: isbn-print Value: 1267199377 – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
| ResultId | 1 |