Bioinformatics Techniques and Theories
This course introduces the fundamental theories, core concepts, and essential techniques of bioinformatics, an interdisciplinary field that integrates biology, statistics, and computer science. Bioinformatics focuses on employing computational methods to process and analyze complex biological data, thereby addressing practical biological problems.
The course covers foundational knowledge of molecular biology and bioinformatics, the use of commonly employed bioinformatics databases, the basic principles and tools for sequence alignment, as well as the application of statistics and machine learning in bioinformatics. Additionally, it delves into advanced topics such as protein information analysis, genome-wide association studies (GWAS), transcriptome data analysis, and systems biology approaches including gene regulatory networks and multi-omics data integration.
This course aims to provide learners with a solid theoretical foundation and practical skills in biology, helping them master core methods and tools in bioinformatics and supporting their exploration of this rapidly evolving field.
The course covers foundational knowledge of molecular biology and bioinformatics, the use of commonly employed bioinformatics databases, the basic principles and tools for sequence alignment, as well as the application of statistics and machine learning in bioinformatics. Additionally, it delves into advanced topics such as protein information analysis, genome-wide association studies (GWAS), transcriptome data analysis, and systems biology approaches including gene regulatory networks and multi-omics data integration.
This course aims to provide learners with a solid theoretical foundation and practical skills in biology, helping them master core methods and tools in bioinformatics and supporting their exploration of this rapidly evolving field.
讲师
日期
2025年03月20日 至 06月12日
位置
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| 周四 | 13:30 - 16:55 | A3-1-103 | ZOOM 12 | 815 762 8413 | BIMSA |
课程大纲
Chapter 1: Molecular Biology and Bioinformatics
Fundamentals of molecular biology: Structure and function of DNA, RNA, and proteins.
Central dogma: Processes and mechanisms of transcription, translation, and replication.
Introduction to bioinformatics: Definition, history, and major applications.
Chapter 2: Common Bioinformatics Databases
Categories of bioinformatics databases: Sequence, structural, and functional databases.
Overview of key databases: GenBank, UniProt, PDB, Ensembl, and others.
Techniques for data retrieval, download, and integration.
Chapter 3: Sequence Alignment and Advanced Analysis
Pairwise sequence alignment: Basics, global and local alignment algorithms (Needleman-Wunsch and Smith-Waterman).
Substitution matrices and scoring standards (e.g., PAM, BLOSUM).
Multiple sequence alignment: Concepts, tools (ClustalW, MUSCLE, MAFFT), and interpretation.
Chapter 4: Statistics and Machine Learning in Bioinformatics
Biostatistics Fundamentals: Descriptive and inferential statistics (mean, variance, t-test, ANOVA, chi-square test).
Data Visualization: Heatmaps, volcano plots, and principal component analysis (PCA).
Machine Learning Basics: Supervised learning, unsupervised learning, and deep learning.
Applications in Bioinformatics: Gene biomarker discovery, disease classification models, and clustering analysis.
Chapter 5: Protein Information Analysis
Protein structure and function: Levels of protein structure (primary to quaternary).
Protein function prediction: Motif, active site, and functional domain identification.
Protein-protein interaction analysis and structural modeling.
Chapter 6: Genomics and Transcriptomics
Whole-genome sequencing and genome assembly techniques.
Genome-wide association studies (GWAS): Principles, workflow, and case studies.
Transcriptome data analysis: RNA-Seq data preprocessing, differential expression analysis.
Single-cell transcriptomics: Techniques and applications.
Chapter 7: Systems Biology
Gene regulatory networks and protein interaction network construction and analysis.
Multi-omics data integration: Combining genomics, transcriptomics, and proteomics.
Fundamentals of molecular biology: Structure and function of DNA, RNA, and proteins.
Central dogma: Processes and mechanisms of transcription, translation, and replication.
Introduction to bioinformatics: Definition, history, and major applications.
Chapter 2: Common Bioinformatics Databases
Categories of bioinformatics databases: Sequence, structural, and functional databases.
Overview of key databases: GenBank, UniProt, PDB, Ensembl, and others.
Techniques for data retrieval, download, and integration.
Chapter 3: Sequence Alignment and Advanced Analysis
Pairwise sequence alignment: Basics, global and local alignment algorithms (Needleman-Wunsch and Smith-Waterman).
Substitution matrices and scoring standards (e.g., PAM, BLOSUM).
Multiple sequence alignment: Concepts, tools (ClustalW, MUSCLE, MAFFT), and interpretation.
Chapter 4: Statistics and Machine Learning in Bioinformatics
Biostatistics Fundamentals: Descriptive and inferential statistics (mean, variance, t-test, ANOVA, chi-square test).
Data Visualization: Heatmaps, volcano plots, and principal component analysis (PCA).
Machine Learning Basics: Supervised learning, unsupervised learning, and deep learning.
Applications in Bioinformatics: Gene biomarker discovery, disease classification models, and clustering analysis.
Chapter 5: Protein Information Analysis
Protein structure and function: Levels of protein structure (primary to quaternary).
Protein function prediction: Motif, active site, and functional domain identification.
Protein-protein interaction analysis and structural modeling.
Chapter 6: Genomics and Transcriptomics
Whole-genome sequencing and genome assembly techniques.
Genome-wide association studies (GWAS): Principles, workflow, and case studies.
Transcriptome data analysis: RNA-Seq data preprocessing, differential expression analysis.
Single-cell transcriptomics: Techniques and applications.
Chapter 7: Systems Biology
Gene regulatory networks and protein interaction network construction and analysis.
Multi-omics data integration: Combining genomics, transcriptomics, and proteomics.
听众
Undergraduate
, Advanced Undergraduate
, Graduate
, 博士后
, Researcher
视频公开
不公开
笔记公开
不公开
语言
中文