Intro to Bioinfo & Comp Bio
1
Course information
1.1
Contributors
2
Introduction
2.1
Brief history of bioinformatics
2.1.1
Protein structure wave
2.1.2
Gene expression wave
2.1.3
Genome sequencing wave
2.1.4
Big data challenge from sequencing
2.2
Should I take this course?
2.2.1
Bioinformatics vs computational biology
2.2.2
Is this class for me?
2.3
Course information
2.3.1
Logistics
2.3.2
X Shirley Liu lab introduction
2.4
Lab 1
2.4.1
Introduction
2.4.2
Introduction to R
2.4.3
Introduction to Bash
2.4.4
Getting started with Cannon
3
High throughput sequencing
3.1
Three generations of sequencing technologies
3.2
FASTQ and FASTQC
3.3
Early sequence alignment (1 with 1)
3.4
Sequence search algorihtms (1 with many)
3.5
Borrow-Wheeler Aligner (many with many)
3.6
Alignment output
4
RNA-seq Quantification
4.1
Introduction to RNA-seq experiment
4.2
RNA quality control and experimental design
4.3
Alignment
4.4
RNA-seq QC
4.5
RNA-seq expression index
4.6
RSEM and Salmon
4.7
RNA-seq read distribution
4.8
Lab 2
4.8.1
STAR tutorial
4.8.2
RSeQC tutorial
4.8.3
RSEM/Salmon Tutorial
<<<<<<< Updated upstream
5
Differential expression, False discovery rate, Gene ontology
5.1
RNA-seq NB distribution
5.2
DESeq2 and variance stabilization
5.3
Multiple hypotheses testing and FWER
5.4
FDR
5.5
GO
=======
5
Differential expression, False discovery rate, Gene ontology
5.1
DESeq2 library normalization
5.2
DESeq2 gene filtering
5.3
DESeq2 variance stabilization
5.4
Multiple hypotheses testing and False Discovery Rate
5.5
Gene Ontology (GO analysis)
5.6
Gene Set Enrichent Analysis (GSEA)
>>>>>>> Stashed changes
6
GSEA, Clustering
6.1
GSEA
6.2
Heatmap and clustering quality
6.3
H-cluster
6.4
K-means
6.5
Pick K and consensus clustering
6.6
Batch effect removal
7
Dimension Reduction
7.1
MDS
7.2
LDA
7.3
PCA
8
Classification
8.1
Intro to machine learning
8.2
Cross validation
8.3
Regression
8.4
Regularization
8.5
KNN
8.6
Decision trees
8.7
Random forest
8.8
SVM
9
Module I Review
9.1
Gene Expression Module Summary
9.2
Gene Expression Analysis Scenarios
10
Transcription Factor Motif Finding
10.1
Transcription regulation
10.2
Motif representation
10.3
EM
10.4
Gibbs sampler
10.5
Gibbs intuition
10.6
Motif finding in eukaryotes
10.7
Known motif database
11
ChIP-seq, Expression Integration
11.1
ChIP-seq
11.2
BWA and MACS
11.3
ChIP-seq QC
11.4
TF interactions (motif)
11.5
TF target genes (expression integration)
12
Epigenetics, DNA Methylation
12.1
Epigenetics
12.2
DNA methylation
12.3
Promoter function
12.4
Gene body function
12.5
Enhancer function
12.6
Repetitive region function
12.7
Early cancer detection
13
Histone Modifications , Chromatin Accessibility
13.1
Nucleosome positions
13.2
Histone modification
13.3
Promoters (bivalent)
13.4
Genes (K36me3, new genes)
13.5
Enhancers (K27ac)
13.6
Super-enhancers
13.7
DNase-seq
13.8
ATAC-seq
14
Long Range Chromatin Interactions
14.1
Chromatin interactions
14.2
HiC
14.3
HiC contact map
14.4
HiC normalization
14.5
Fractal globule
14.6
Loops
14.7
Domains
14.8
Compartments
14.9
Phase separation
15
Hidden Markov Model
15.1
Intro to HMM
15.2
Pb1: Forward & backward procedure
15.3
Pb2: Viterbi algorithm
15.4
Pb3: Parameter estimation
15.5
HMM application
16
Module II Review
16.1
Module II Review
16.2
Practive Questions
17
SNP and GWAS
17.1
SNP and LD
17.2
Family-based vs case-control association studies
17.3
GWAS studies and catalog
17.4
GTEx and eQTL
18
GWAS and Epigenomics
18.1
Find tissue / cell type
18.2
Identify causal SNPs and genes
18.3
Predict phenotypes
19
Single-cell RNA-seq (1)
19.1
Intro to scRNA-seq
19.2
Smart, Droplet, microwell, SCI-based
19.3
QC
19.4
Normalization
19.5
Imputation
19.6
Dimension reduction
19.7
Clustering
19.8
t-SNE and UMAP
20
Single-cell RNA-seq (2)
20.1
Annotate scRNA-seq clusters
20.2
Differential expression
20.3
Batch effect removal
20.4
Pseudotime
20.5
Overload 10X
20.6
Other applications (CITE-seq, multi-seq, spatial transcriptomics)
21
scATAC-seq
21.1
Intro to scATAC-seq
21.2
Sample and cell QC
21.3
Dimension reduction, clustering & visualization
21.4
Differential peaks and annotations
21.5
Integration with scRNA-seq
22
Module III Review
22.1
Module III Review
23
Cancer Genome Sequencing , Mutation analyses
23.1
Intro to TCGA
23.2
Cancer mutation characterization
23.3
Cancer mutation patterns
23.4
Tumor purity and clonality
23.5
Interpret tumor mutations
23.6
Find cancer genes
23.7
Summary and future
24
Cancer Subtyping, Survival Analyses
24.1
TCGA expression
24.2
Tumor subtypes
24.3
Survival analysis
24.4
GoF Oncogenes and LoF TS
24.5
Chromatin regulator mutations in cancer
24.6
DNA methylation and CIMP
25
Targeted Therapy, Drug Resistance, Compound and Genetic Screens
25.1
Hallmarks of cancer
25.2
Chemo vs targeted therapy
25.3
Drug resistance
25.4
Synthetic lethality
25.5
Precision medicine
25.6
Tumor (bulk vs scRNA-seq), mice, cell lines
25.7
Compound screens
25.8
Genetic screens
25.9
Tumor heterogeneity
26
Cancer Immunotherapy (1)
26.1
Systemic immunotherapy
26.2
Personalized immunotherapy
26.3
HLA and neoantigens
26.4
Tumor immune deconvolution
26.5
T cell signaling (PD1/PDL1, etc)
26.6
Other immune-cells (scRNA-seq)
27
Cancer Immunotherapy (2)
27.1
TCR analysis
27.2
BCR analysis
27.3
Microbiome
27.4
Immunotherapy response biomarkers
27.5
Targeted therapy as immune-modulators
27.6
Epigenetic therapy as immune-modulators
28
CRISPR Screens
28.1
CRISPR and KO
28.2
CRISPRa and CRISPRi
28.3
CRISPR design and outcome
28.4
CRISPR screens & DepMap
28.5
CRISPR screen analysis
28.6
CRISPR screens in drug response
28.7
CRISPR screens in immunology
28.8
Enhancer CRISPR screen
28.9
CRISPR screens + scRNA-seq
29
Module IV Review and Course Review
29.1
Module IV Review
29.2
Course Review
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Introduction to Bioinformatics and Computational Biology
Chapter 6
GSEA, Clustering
6.1
GSEA
6.2
Heatmap and clustering quality
6.3
H-cluster
6.4
K-means
6.5
Pick K and consensus clustering
6.6
Batch effect removal