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Computational Methods in Functional Genomics and Transcriptional Dynamics: Systems-Level Insights Into Neurodegeneration and Neurodevelopmental Disorders
Computational Methods in Functional Genomics and Transcriptional Dynamics: Systems-Level Insights Into Neurodegeneration and Neurodevelopmental Disorders
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153027
- ISBN
- 9798346877318
- DDC
- 574
- 저자명
- Teyssier, Noam.
- 서명/저자
- Computational Methods in Functional Genomics and Transcriptional Dynamics: Systems-Level Insights Into Neurodegeneration and Neurodevelopmental Disorders
- 발행사항
- [Sl] : University of California, San Francisco, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 240 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
- 주기사항
- Advisor: Kampmann, Martin.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2024.
- 초록/해제
- 요약This dissertation presents a suite of computational methods and theoretical frameworks that advance our understanding of functional genomics, particularly in the context of single-cell analysis and CRISPR screening. Through the development of novel algorithms and analytical approaches, this work addresses critical challenges in processing, analyzing, and interpreting complex genomic data.The research encompasses several interconnected areas. First, it introduces innovative approaches for studying cis-regulatory elements through massively parallel reporter assays and CRISPR interference screens, revealing distinct transcriptional networks in dementia and identifying hundreds of functional regulatory variants. Second, it presents a systematic analysis of autism spectrum disorder (ASD) risk genes during cortical neurogenesis, uncovering convergent cellular phenotypes and implicating specific molecular pathways in neurodevelopment.The dissertation also introduces several computational tools that significantly improve existing methods in genomic analysis. These include GIA (Genomic Interval Arithmetic), a high-performance toolkit for genomic interval analysis that achieves 2-20x speed improvements over existing tools; geomux, a novel algorithm for cell identity demultiplexing in single-cell experiments that demonstrates superior accuracy in low multiplicity of infection settings; and a comprehensive CRISPR screening analysis toolkit comprising sgcount, crispr-screen, and screenviz, which streamlines the analysis of CRISPR screen data through efficient processing, statistical analysis, and visualization.Finally, the work develops a theoretical framework for modeling gene regulatory networks, progressing from linear to increasingly sophisticated non-linear models. This culminates in a Hill-function product model capable of capturing complex biological phenomena such as multiple stable states and oscillatory behavior, while maintaining mathematical rigor and biological plausibility.Throughout this body of work, there is a consistent emphasis on developing methods that are not only powerful and flexible but also accessible to the broader scientific community. By prioritizing computational efficiency, mathematical rigor, and user-friendliness, this research aims to democratize advanced genomic analyses and accelerate discovery across the life sciences.
- 일반주제명
- Bioinformatics
- 일반주제명
- Systematic biology
- 일반주제명
- Neurosciences
- 일반주제명
- Genetics
- 키워드
- Systems biology
- 기타저자
- University of California, San Francisco Biological and Medical Informatics
- 기본자료저록
- Dissertations Abstracts International. 86-06B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211153027
■006m o d
■007cr#unu||||||||
■020 ▼a9798346877318
■035 ▼a(MiAaPQ)AAI31634367
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aTeyssier, Noam.▼0(orcid)0000-0003-4001-6296
■24510▼aComputational Methods in Functional Genomics and Transcriptional Dynamics: Systems-Level Insights Into Neurodegeneration and Neurodevelopmental Disorders
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a240 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-06, Section: B.
■500 ▼aAdvisor: Kampmann, Martin.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2024.
■520 ▼aThis dissertation presents a suite of computational methods and theoretical frameworks that advance our understanding of functional genomics, particularly in the context of single-cell analysis and CRISPR screening. Through the development of novel algorithms and analytical approaches, this work addresses critical challenges in processing, analyzing, and interpreting complex genomic data.The research encompasses several interconnected areas. First, it introduces innovative approaches for studying cis-regulatory elements through massively parallel reporter assays and CRISPR interference screens, revealing distinct transcriptional networks in dementia and identifying hundreds of functional regulatory variants. Second, it presents a systematic analysis of autism spectrum disorder (ASD) risk genes during cortical neurogenesis, uncovering convergent cellular phenotypes and implicating specific molecular pathways in neurodevelopment.The dissertation also introduces several computational tools that significantly improve existing methods in genomic analysis. These include GIA (Genomic Interval Arithmetic), a high-performance toolkit for genomic interval analysis that achieves 2-20x speed improvements over existing tools; geomux, a novel algorithm for cell identity demultiplexing in single-cell experiments that demonstrates superior accuracy in low multiplicity of infection settings; and a comprehensive CRISPR screening analysis toolkit comprising sgcount, crispr-screen, and screenviz, which streamlines the analysis of CRISPR screen data through efficient processing, statistical analysis, and visualization.Finally, the work develops a theoretical framework for modeling gene regulatory networks, progressing from linear to increasingly sophisticated non-linear models. This culminates in a Hill-function product model capable of capturing complex biological phenomena such as multiple stable states and oscillatory behavior, while maintaining mathematical rigor and biological plausibility.Throughout this body of work, there is a consistent emphasis on developing methods that are not only powerful and flexible but also accessible to the broader scientific community. By prioritizing computational efficiency, mathematical rigor, and user-friendliness, this research aims to democratize advanced genomic analyses and accelerate discovery across the life sciences.
■590 ▼aSchool code: 0034.
■650 4▼aBioinformatics
■650 4▼aSystematic biology
■650 4▼aNeurosciences
■650 4▼aGenetics
■653 ▼aComputational biology
■653 ▼aFunctional genomics
■653 ▼aSingle-cell sequencing
■653 ▼aSystems biology
■653 ▼aTheoretical biology
■690 ▼a0715
■690 ▼a0423
■690 ▼a0317
■690 ▼a0369
■71020▼aUniversity of California, San Francisco▼bBiological and Medical Informatics.
■7730 ▼tDissertations Abstracts International▼g86-06B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164654▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
