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2018 STAT115 Lecture 1.1. Introduction to Computational Biology
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2018 STAT115 Lecture 1.2. Introduction to Computational Biology
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2018 STAT115 Lecture 1.3. Class Information
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2018 STAT115 Lect 1.4. Microarrays
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2018 STAT115 Lect 3.1. Differential Expression
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2018 STAT115 Lect 3.2. LIMMA
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2018 STAT115 Lect 3.3. Multiple Hypothesis Testing
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2018 STAT115 Lect 3.4. False Discovery Rate
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2018 STAT115 Lect 4.1. Hierarchical Clustering
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2018 STAT115 Lect 4.2. K-means clustering
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2018 STAT115 Lect 4.3. Batch Effect Removal
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2018 STAT115 Lect 4.4. Gene Ontology
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2018 STAT115 Lect 5.1. GO and GSEA
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2018 STAT115 Lect 5.2. Unsupervised Learning
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2018 STAT115 Lect 5.3. Principal Component Analysis
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2018 STAT115 Lect 5.4. Supervise Learning
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2018 STAT115 Lect 6.1. Supervised Learning
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2018 STAT115 Lect 6.2. High Throughput Sequencing
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2018 STAT115 Lect 6.3. FASTQC
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2018 STAT115 Lect 6.4. Burrows-Wheeler Read Alignment
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2018 STAT115 Lect 7.1. SAM and BED Files
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2018 STAT115 Lect 7.2. RNA-seq Experimental Design
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2018 STAT115 Lect 7.3. RNA-seq Alignment and QC
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2018 STAT115 Lect 7.4. RNA-seq Quantification
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2018 STAT115 Lect 7.5. Differential RNA-seq Analyses
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2018 STAT115 Lect 8.1. RNA-seq Isoform Inference
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2018 STAT115 Lect 8.2. RNA-seq Alternative Splicing
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2018 STAT115 Lect 8.3. Single Cell RNA-seq
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2018 STAT115 Lect 9.1. scRNA-seq t-SNE
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2018 STAT115 Lect 9.2. scRNA-seq Data Analysis
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2018 STAT115 Lect 9.3. Transcription Factor Motifs
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2018 STAT115 Lect 10.1. Motif Finding Expectation Maximization
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2018 STAT115 Lect 10.2. Motif Finding Gibbs Sampler
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2018 STAT115 Lect 10.3. Motif Finding Motif Scores
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2018 STAT115 Lect 10.4. Motif Finding Summary
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2018 STAT115 Lect 11.1. ChIP-seq
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2018 STAT115 Lect 11.2. ChIP-seq QC and Peak Calling
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2018 STAT115 Lect 11.3. ChIP-seq Target Genes
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2018 STAT115 Lect 11.4. ChIP-seq Integration with Expression
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2018 STAT115 Lect 12.1. Motif Finding in ChIP-seq
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2018 STAT115 Lect 12.2. TF Function from ChIP-seq
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2018 STAT115 Lect 12.3. Cistrome
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2018 STAT115 Lect 13.1. Epigenetics
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2018 STAT115 Lect 13.2. DNA Methylation
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2018 STAT115 Lect 13.3. DNA Methylation 2
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2018 STAT115 Lect 13.4. Nucleosome Positioning
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2018 STAT115 Lect 15.1. Histone Mark
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2018 STAT115 Lect 15.2. Chromatin Dynamics
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2018 STAT115 Lect 15.3. Chromatin Accessibility
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2018 STAT115 Lect 16.1. HiC
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2018 STAT115 Lect 16.2. Topologically Associated Domains
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2018 STAT115 Lect 16.3. Loop Extrusion Model
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2018 STAT115 Lect 16.4. Regulatory Network
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2018 STAT115 Lect 17.1. SNP and LD
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2018 STAT115 Lect 17.2. Association Studies
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2018 STAT115 Lect 17.3. Biases in GWAS
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2018 STAT115 Lect 17.4 Q&A
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2018 STAT115 Lect 18.1. GWAS Interpret From Literature
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2018 STAT115 Lect 18.2. GWAS Interpret From Expression
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2018 STAT115 Lect 18.3. GWAS Interpret From Epigenetics
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2018 STAT115 Lect 18.4. cis- vs trans-eQTL
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2018 STAT115 Lect 19.1. Tumor Sequencing
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2018 STAT115 Lect 19.2. Survival Analyses
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2018 STAT115 Lect 19.3. Tumor Mutation Calling
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2018 STAT115 Lect 19.4. Tumor Mutation Patterns
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2018 STAT115 Lect 20.1. Targeted Therapy and Cell Line Drug Screens
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2018 STAT115 Lect 20.2. shRNA and CRISPR Screens
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2018 STAT115 Lect 20.3. CRISPR Screen Data Analyses
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2018 STAT115 Lect 20.4. Drug Resistance
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2018 STAT115 Lect 21.1. Cancer Immunotherapy
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2018 STAT115 Lect 21.2. Immune Infiltration
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2018 STAT115 Lect 21.3. Immune Repertoires
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2018 STAT115 Lect 21.4. Immune Regulators
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2018 STAT115 Lect 22.1. TCGA Epigenetic Profiling
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2018 STAT115 Lect 22.2. Epigenetic Drugs 5AZA
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2018 STAT115 Lect 22.3. Targeted Epigenetic Therapy
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2018 STAT115 Lect 23.1. Deep Learning
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2018 STAT115 Lect 23.2. Deep Learning Continued
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2018 STAT115 Lect 24.1. Final Review
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2018 STAT115 Lect 24.2. Levels of Bioinformatics
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2018 STAT115 Lect 24.3. Final Exam Example Questions
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2018 STAT115 Lect 24.4. Final Exam Example Questions 2
Description:
Dive into a comprehensive lecture series on computational biology, covering a wide range of topics from microarray analysis to deep learning applications in bioinformatics. Learn about differential expression, clustering techniques, gene ontology, high-throughput sequencing, RNA-seq analysis, single-cell RNA-seq, transcription factor motifs, ChIP-seq, epigenetics, chromatin dynamics, genome-wide association studies, tumor sequencing, cancer immunotherapy, and more. Explore various bioinformatics tools, statistical methods, and data analysis techniques used in genomics research. Gain insights into the latest advancements in computational biology and their applications in understanding complex biological systems and diseases.

STAT115 2018

Harvard University
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2018 STAT115 Lecture 1.1. Introduction to Computational Biology