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
Burrows-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
5
Differential expression, FDR, GO, and GSEA
5.1
DESeq2 library normalization
5.2
DESeq2 variance stabilization
5.3
Multiple hypotheses testing and False Discovery Rate
5.4
DESeq2 gene filtering
5.5
Gene Ontology (GO analysis)
5.6
Gene Set Enrichent Analysis (GSEA)
5.7
DESeq2 tutorial
6
Clustering
6.1
Heatmap and clustering quality
6.2
Hierarchical cluster
6.3
K means cluster
6.4
Pick K and consensus clustering
6.5
Batch effect removal
6.6
Lab3
6.6.1
PCA tutorial
6.6.2
Clustering tutorial
6.6.3
Combat tutorial
6.6.4
DESeq2 Tutorial
6.6.5
DAVID/GSEA Tutorial
7
Dimension Reduction
7.1
Principal Component Analysis: idea behind PCA.
7.2
Principal Component Analysis: PCA applications.
7.3
Multidimensional Scaling (MDS)
7.4
Linear discriminant Analysis (LDA)
8
Classification
8.1
Introduction
8.2
Supervised learning
8.3
Cross validation
8.4
Regression
8.5
Regularization
8.5.1
Ridge regression
8.5.2
LASSO regression
8.6
KNN
8.7
Decision trees
8.8
Random forest
8.9
SVM
8.10
Lab 4
8.10.1
K-Nearest Neighbors tutorial
8.10.2
Regression/Ridge/LASSO Tutorial
8.10.3
Logistic Regression Tutorial
8.10.4
Support Vector Machine Tutorial
8.10.5
Random Forest Tutorial
9
Module I Review
9.1
Module I review
9.2
Analysis Scenario 1
9.3
Analysis Scenario 2
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
Motif finding in eukaryotes, and ChIP-seq
11.2
MACS and ChIP-seq QC
11.3
Identify TF interactions from ChIP-seq motifs
11.4
TF target genes and expression integration
11.5
Lab 5
11.5.1
MACS Tutorial
11.5.2
ChIP-seq QC Tutorial
11.5.3
TF Motif Finding Tutorial
11.5.4
TF Collaborator Tutorial
12
Epigenetics, DNA Methylation
12.1
Intro to DNA Methylation
12.2
DNA Methylation Pattern and Function
12.3
DNA Methylation in Diseases
12.4
Techniques to Measure DNA Methylation
13
Histone Modifications , Chromatin Accessibility
13.1
Nucleosome Positioning
13.2
Introduction to Histone Modifications
13.3
Infer Transcription Factor Binding from Histone Mark Dynamics
13.4
Using Histone Marks to Infer Gene Functions
13.5
Introduction to DNase-seq and ATAC-seq
13.6
Infer TF from Differential Genes Using LISA
13.7
Caution on DNase/ATAC-seq footprint analysis
13.8
Summary of Epigenetics and Chromatin
13.9
Lab 6
13.9.1
ChIP-seq Expression Integration
13.9.2
Cistrome-GO Tutorial
13.9.3
ATAC-seq Analysis and LISA Tutorial
14
Hidden Markov Model
14.1
Markov Chain
14.2
Hidden Markov Model
14.3
Hidden Markov Model Forward Procedure
14.4
Hidden Markov Model Backward Procedure
14.5
HMM Forward-Backward Algorithm
14.6
Viterbi Algorithm
14.7
Baum Welch Algorithm Intuition
14.8
HMM Bioinformatics Applications
15
HiC
15.1
Introduction to Chromatin Interaction and Organization
15.2
Methods to Investigate 3D Genome Organization
15.3
Topologically Associating Domains
15.4
TAD Function and Loop Anchors
15.5
Chromatin Compartments
15.6
Computational Methods to Call Chromatin Loops
15.7
Variations of Chromatin Interaction Technologies
15.8
Resources for Exploring 3D Genomes
15.9
Lab 7
15.9.1
BS-seq and Bismark Tutorial
15.9.2
Tutorial on Associating DNA Methylation with Expression
15.9.3
HiC Analysis Tutorial
16
Module II Review
16.1
Module II Review
16.2
Module II Analysis Scenarios
17
SNP and GWAS
17.1
SNP, LP, and Association Studies
17.2
GWAS Studies and eQTL Analysis
17.3
Lab 8
17.3.1
HW4 FAQ & cooler
17.3.2
Pikachu&HiGlass
17.3.3
HMM
18
GWAS and Epigenomics
18.1
Intro Functional Annotate GWAS
18.2
GWAS Functional Enrichment
18.3
Find Causal SNPs
18.4
Predict disease risk
19
Single-cell RNA-seq (1)
19.1
Intro to scRNA-seq
19.2
scRNA seq techniques
19.3
scRNA seq preprocessing and QC
19.4
Cleaning up expression matrix
20
Single-cell RNA-seq (2)
20.1
scRNA seq dimension reduction
20.2
Clustering and projections
20.3
Pseudo time and RNA velocity
20.4
Clustering by genotype and CITE seq
21
scATAC-seq
21.1
Single-Cell ATAC-seq Technique
21.2
Single-Cell ATAC-seq Pre-Processing and QC
21.3
Single-Cell ATAC-seq Analysis
21.4
scATAC-seq Downstream Analyses and scRNA-seq Integration
21.5
Lab 9
21.5.1
MAESTRO tutorial
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
Tumor Subtypes
24.2
Survival analysis
24.3
Oncogenes and Tumor Suppressor Mutations
24.4
Cancer Epigenetics
25
Targeted Therapy and Precision Medicine
25.1
Introduction to Targeted Therapy and Precision Medicine
25.2
Resistance to targeted therapy
25.3
Model system chemical and genetic screens
25.4
Overcoming resistance to targeted therapy
26
Cancer Immunotherapy (1)
26.1
Intro to Cancer Immunotherapy
26.2
HLA and Neoantigen Presentation
26.3
Immune Cell Infiltration in Tumors
26.4
T Cell Receptor Repertoires in Cancer Immunology
26.5
Lab 10
26.5.1
TCGA exploration
26.5.2
LIMMA on microarray data
26.5.3
Survival analysis
27
Cancer Immunotherapy (2)
27.1
B Cell Receptor Repertoires in Tumors
27.2
T Cell Activation and Dysfunction
27.3
NK Cells and Macrophages in Tumor Immunity
27.4
Cancer Immunotherapy Response Biomarkers
27.5
Improving Immunotherapy Response
27.6
Lab 11
27.6.1
Cancer mutations and driver genes
27.6.2
CRIPSR screen
27.6.3
Cancer immunology
28
CRISPR Screens
28.1
Introduction to CRISPR and CRISPR Screens
28.2
Computational Resources for CRISPR and Screens
28.3
Cancer Cell Vulnerability from CRISPR Screens
28.4
Immune Related CRISPR Screens
28.5
Depmap tutorial
29
Module IV Review and Course Review
29.1
Module IV Review
29.2
Final Course Review
29.3
Final Exam Preparations
29.4
Levels of Bioinformatics and Preparing for the Future
Published with bookdown
Introduction to Bioinformatics and Computational Biology
Chapter 14
Hidden Markov Model
14.1
Markov Chain
14.2
Hidden Markov Model
14.3
Hidden Markov Model Forward Procedure
14.4
Hidden Markov Model Backward Procedure
14.5
HMM Forward-Backward Algorithm
14.6
Viterbi Algorithm
14.7
Baum Welch Algorithm Intuition
14.8
HMM Bioinformatics Applications