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2020 STAT115 Lect1.1 Bioinfo History
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2020 STAT115 Lect1.2 Big Data Challenge
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2020 STAT115 Lect1.3 Bioinfo vs Comp Bio
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2020 STAT115 Lect1.4 Course Info
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STAT115 Chapter 3.1 Three Generations of Sequencing
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STAT115 Chapter 3.2 FASTQ and FASTQC
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STAT115 Chapter 3.4 BLAST and Suffix Arrays
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STAT115 Chapter 3.5.1 BWT and LF Mapping
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STAT115 Chapter 3.5.2 Borrows-Wheeler Alignment
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STAT115 Chapter 3.6 SAM and BAM files
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2020 STAT115 Lect3.1 RNA-seq Experimental Design
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2020 STAT115 Lect3.2 RNA-seq Alignment and QC
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2020 STAT115 Lect3.3 RNA-seq Quantification
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2020 STAT115 Lect3.4 RNA-seq Read Distribution
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STAT115 Chapter 5.2 Differential RNA-seq
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STAT115 Chapter 5.3 Multiple Hypotheses Testing and False Discovery Rate
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STAT115 Chapter 5.5 Gene Ontology
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STAT115 Chapter 5.6 Gene Set Enrichment Analyses
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STAT115 Chapter 6.1/2 Hierarchical Clustering
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STAT115 Chapter 6.3 K-means Clustering
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STAT115 Chapter 6.4 Considerations of Kmeans Clustering
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2020 STAT115 Lect6.1 KNN and MDS
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2020 STAT115 Lect6.2 PCA
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STAT115 Chapter 6.5 Batch Effect Removal
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2020 STAT115 Lect8.1 scRNA-seq
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2020 STAT115 Lect8.2 Processing and QC scRNA-seq
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2020 STAT115 Lect8.3 scRNA-seq Clustering and Visualization
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2020 STAT115 Lect8.4 Differential Expression in scRNA-seq
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2020 STAT115 Lect9.1 scRNA-seq Batch Effect Removal
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2020 STAT115 Lect9.2 Gene Expression Module Summary
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2020 STAT115 Lect9.3 Gene Expression Analysis Scenario
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STAT115 Chapter 10.1 Transcription Regulation
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STAT115 Chapter 10.2 Expectation Maximization for Motif Finding
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STAT115 Chapter 10.3 Gibbs Sampling for Motif Finding
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STAT115 Chapter 10.4 Motif Finding General Practices
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STAT115 Chapter 11.1 ChIP-seq
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STAT115 Chapter 11.2 ChIP-seq Peak Calling with MACS and QC
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STAT115 Chapter 11.3 TF Interactions from ChIP-seq
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STAT115 Chapter 11.4 TF Target Genes from ChIP-seq
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2020 STAT115 Lect12.1 Epigenetics
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2020 STAT115 Lect12.2 DNA Methylation
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2020 STAT115 Lect12.3 DNA Methylation Function
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2020 STAT115 Lect12.4 Nucleosome Positioning
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2020 STAT115 Lect13.1 Histone Modifications
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2020 STAT115 Lect13.2 Histone marks on enhancers
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2020 STAT115 Lect13.3 Histone marks on genes
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STAT115 Chapter 13.7 DNase-seq
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2020 STAT115 Lect13.5 ATAC-seq
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2020 STAT115 Lect15.1 HiC Introduction
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2020 STAT115 Lect15.2 Topologically Associating Domains
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2020 STAT115 Lect15.3 Chromatin Compartments
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2020 STAT115 Lect15.4 Regulatory Network
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2020 STAT115 Lect16.1 Intro to Single-Cell ATAC-seq
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2020 STAT115 Lect16.2 Preprocessing and QC scATAC-seq
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2020 STAT115 Lect16.3 Analysis of scATAC-seq
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2020 STAT115 Lect16.4 Integrating scATAC-seq with scRNA-seq
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2020 STAT115 Lect17.1 Module II Review
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2020 STAT115 Lect17.2 Module II Practice Scenarios
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Overview of Cistrome Related Tools from Liu Lab
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2020 STAT115 Lect17.3 SNP and GWAS Intro
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2020 STAT115 Lect17.4 GWAS Artifacts and eQTL
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STAT115 Chapter 18.1 Intro Functional Annotate GWAS
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STAT115 Chapter 18.2 GWAS Functional Enrichment
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STAT115 Chapter 18.3 Find Causal SNPs
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STAT115 Chapter 18.4 Predict disease risk
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STAT115 GWAS Catalogue
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STAT115 HaploReg
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STAT115 LD Link
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STAT115 Chapter 23.1 Introduction to Cancer Genome Analysis
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STAT115 Chapter 23.2 Cancer. Mutation Characterization
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STAT115 Chapter 23.3 Cancer Mutation Patterns
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STAT115 Chapter 23.4 Tumor Purity and Clonality
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STAT115 Chapter 23.5 Interpret Tumor Mutations
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STAT115 Chapter 23.6 Find Cancer Genes
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STAT115 Chapter 23.7 Summary and Future
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STAT115 OpenCRAVAT Demo
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STAT115 Chapter 24.1 Tumor Subtypes
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STAT115 Chapter 24.2 Survival Analysis
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STAT115 Chapter 24.3 Oncogenes and Tumor Suppressor Mutations
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STAT115 Chapter 24.4 Cancer Epigenetics
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2020 STAT115 Lect20.5 Cancer Hallmarks
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STAT115 Chapter 26.1 Intro to Cancer Immunotherapy
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STAT115 Chapter 26.2 HLA and Neoantigen Presentation
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2020 STAT115 Lect22.3 Tumor Immune Deconvolution
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2020 STAT115 Lect22.4 Immune Receptor Repertoires
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2020 STAT115 Lect23.1 Immune Signaling
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2020 STAT115 Lect23.2 Immunotherapy Biomarkers
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2020 STAT115 Lect23.3 CRISPR Screens
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2020 STAT115 Lect23.4 CRISPR Screen Computational Challenges
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2020 STAT115 Lect24.1 Module III Review
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2020 STAT115 Lect24.2 Course Review
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2020 STAT115 Lect24.3 Final Logistics
Description:
Explore a comprehensive bioinformatics course from Harvard University covering key topics in genomics, transcriptomics, and cancer biology. Delve into sequencing technologies, RNA-seq analysis, clustering methods, single-cell genomics, epigenetics, and GWAS. Learn about cancer genome analysis, tumor mutations, immunotherapy, and CRISPR screens. Gain practical skills in data analysis, quality control, and interpretation of genomic data. Master essential bioinformatics tools and techniques used in cutting-edge research and clinical applications.
STAT115 2020
Harvard University
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