Dissertation/ Thesis

Assessing the discoverability of variant proteins causing rare forms of paediatric diabetes using proteogenomics

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Assessing the discoverability of variant proteins causing rare forms of paediatric diabetes using proteogenomics
Συγγραφείς: Naidoo, Lorensha
Συνεισφορές: Patterton, Hugh-George, Vaudel, Marc, Stellenbosch University. Faculty of Science. Centre for Bioinformatics & Computational Biology.
Στοιχεία εκδότη: Stellenbosch University
Έτος έκδοσης: 2025
Συλλογή: Stellenbosch University: SUNScholar Research Repository
Θεματικοί όροι: Diabetes in children -- Genetic aspects, Proteomics -- Data processing, Proteins -- Analysis, Peptides -- Analysis, Spectrum analysis -- Data processing, Machine learning -- Computer simulation, Bayesian statistical decision theory -- Computer simulation, UCTD
Περιγραφή: Thesis (MSc)--Stellenbosch University, 2025. ; Naidoo, L. 2025. Assessing the discoverability of variant proteins causing rare forms of paediatric diabetes using proteogenomics. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/fa2ce23c-42d1-4183-b3ba-b969d378e482 ; ENGLISH ABSTRACT: Monogenic diabetes is a rare form of paediatric diabetes caused by a pathogenic variant occurring in a single gene associated with insulin production from pancreatic β-cells, resulting in hyperglycaemic complications. Maturity-onset diabetes of the young (MODY) is a subtype of monogenic diabetes, accounting for 2 – 5% of diabetic cases. Patient classification has revealed undiagnosed symptomatic cohorts speculated to result from unknown genetic variants. Identifying these variants is essential for advancing precision medicine and providing specialised medical care. Proteogenomics allows for the identification of alternative forms of proteins resulting from genomic variation. Protein samples are processed using mass spectrometry to obtain peptide sequences that are then annotated to a sequence database using search engines, e.g. SEQUEST and X!Tandem. Peptide-spectrum matches (PSMs) with varying confidence scores are produced; however, there is no clear distinction between correct and incorrect PSMs. Additionally, distinguishing variant PSMs from canonical PSMs remains challenging due to their low frequency and sequence similarity. The target-decoy approach (TDA) is a common method for classifying correct and incorrect PSMs and is used in existing PSM processing tools, such as Percolator. Target sequences are peptide sequences from proteomic databases, while decoy sequences are artificially generated to serve as a null model for error rate estimation. To the knowledge of this work, the TDA has not been implemented towards improving the discrimination performance of variant PSMs. To this end, this study conducts an exploratory analysis to improve the classification of ...
Τύπος εγγράφου: thesis
Περιγραφή αρχείου: xiv, 143 pages : illustrations; application/pdf
Γλώσσα: unknown
Relation: https://scholar.sun.ac.za/handle/10019.1/134730
Διαθεσιμότητα: https://scholar.sun.ac.za/handle/10019.1/134730
Rights: Stellenbosch University
Αριθμός Καταχώρησης: edsbas.CDF890BC
Βάση Δεδομένων: BASE
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  Data: Patterton, Hugh-George<br />Vaudel, Marc<br />Stellenbosch University. Faculty of Science. Centre for Bioinformatics & Computational Biology.
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  Data: 2025
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  Data: <searchLink fieldCode="DE" term="%22Diabetes+in+children+--+Genetic+aspects%22">Diabetes in children -- Genetic aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Proteomics+--+Data+processing%22">Proteomics -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Proteins+--+Analysis%22">Proteins -- Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Peptides+--+Analysis%22">Peptides -- Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Spectrum+analysis+--+Data+processing%22">Spectrum analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning+--+Computer+simulation%22">Machine learning -- Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+statistical+decision+theory+--+Computer+simulation%22">Bayesian statistical decision theory -- Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22UCTD%22">UCTD</searchLink>
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  Label: Description
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  Data: Thesis (MSc)--Stellenbosch University, 2025. ; Naidoo, L. 2025. Assessing the discoverability of variant proteins causing rare forms of paediatric diabetes using proteogenomics. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/fa2ce23c-42d1-4183-b3ba-b969d378e482 ; ENGLISH ABSTRACT: Monogenic diabetes is a rare form of paediatric diabetes caused by a pathogenic variant occurring in a single gene associated with insulin production from pancreatic β-cells, resulting in hyperglycaemic complications. Maturity-onset diabetes of the young (MODY) is a subtype of monogenic diabetes, accounting for 2 – 5% of diabetic cases. Patient classification has revealed undiagnosed symptomatic cohorts speculated to result from unknown genetic variants. Identifying these variants is essential for advancing precision medicine and providing specialised medical care. Proteogenomics allows for the identification of alternative forms of proteins resulting from genomic variation. Protein samples are processed using mass spectrometry to obtain peptide sequences that are then annotated to a sequence database using search engines, e.g. SEQUEST and X!Tandem. Peptide-spectrum matches (PSMs) with varying confidence scores are produced; however, there is no clear distinction between correct and incorrect PSMs. Additionally, distinguishing variant PSMs from canonical PSMs remains challenging due to their low frequency and sequence similarity. The target-decoy approach (TDA) is a common method for classifying correct and incorrect PSMs and is used in existing PSM processing tools, such as Percolator. Target sequences are peptide sequences from proteomic databases, while decoy sequences are artificially generated to serve as a null model for error rate estimation. To the knowledge of this work, the TDA has not been implemented towards improving the discrimination performance of variant PSMs. To this end, this study conducts an exploratory analysis to improve the classification of ...
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      – Text: unknown
    Subjects:
      – SubjectFull: Diabetes in children -- Genetic aspects
        Type: general
      – SubjectFull: Proteomics -- Data processing
        Type: general
      – SubjectFull: Proteins -- Analysis
        Type: general
      – SubjectFull: Peptides -- Analysis
        Type: general
      – SubjectFull: Spectrum analysis -- Data processing
        Type: general
      – SubjectFull: Machine learning -- Computer simulation
        Type: general
      – SubjectFull: Bayesian statistical decision theory -- Computer simulation
        Type: general
      – SubjectFull: UCTD
        Type: general
    Titles:
      – TitleFull: Assessing the discoverability of variant proteins causing rare forms of paediatric diabetes using proteogenomics
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            NameFull: Vaudel, Marc
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              Type: published
              Y: 2025
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