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GEN242
Spring 2018
Home
Introduction
Syllabus
Internal
Internal Resources
Schedule
Course Schedule
Lecture Slides
Course Introduction
Molbiol Basics
Databases and Software
DNA Sequencing
Introduction to R
Sequence Alignments
Programming in R
Multiple Alignments
Short Read Alignments
NGS Analysis Basics
Gene Expression Analysis
NGS Workflows
ChIP-Seq Overview
VAR-Seq Overview
Gene Annotation and Ontologies
Sequence Assembly
Cluster Analysis
Profile HMMs
Introduction to Phylogenetics
Homework
HW1 - Online Tools
HW2 - Biocluster/Linux
HW3 - Introduction to R
HW4 - Pairwise Alignments
HW5 - Programming in R
HW6 - Sequence Analysis
HW7 - RNA-Seq Analysis
GitHub
Table of Content
GitHub in GEN242
Installation
Git from Command-line
GitHub from Command-line
Exercise
Online file upload
GitHub from RStudio
Linux & HPCC Cluster
Table of Content
HPCC Cluster
Linux Basics
Software & Module System
Big Data Storage
Queueing System Slurm
Text Editors
Nvim-R-Tmux
Introduction to R
1. Overview
2. R Package Repositories
3. Installation of R Packages
4. Getting Around
5. Basic Syntax
6. Data Types
7. Data Objects
8. Important Utilities
9. Operators and Calculations
10. Reading and Writing External Data
11. Useful R Functions
12. dplyr Environment
13. SQLite Databases
14. Graphics in R
15. Analysis Routine
16. R Markdown
17. Shiny Web Apps
18. Session Info
19. References
Programming in R
1. Overview
2. Control Structures
3. Loops
4. Functions
5. Useful Utilities
6. Running R Scripts
7. Building R Packages
8. Programming Exercises
9. Homework 5
10. Session Info
11. References
NGS Analysis Basics
1. Overview
2. Package Requirements
3. Strings in R Base
4. Sequences in Bioconductor
5. NGS Sequences
6. Range Operations
7. Transcript Ranges
8. Homework 6
9. Session Info
10. References
NGS Workflow Overview
1. Introduction
2. Getting Started
3. Workflow overview
4. Workflow templates
5. Version information
6. References
RNA-Seq Workflow
1. Introduction
2. Samples and environment settings
3. Read preprocessing
4. Alignments
5. Read quantification
6. Analysis of DEGs
7. GO term enrichment analysis
8. Clustering and heat maps
9. Version Information
10. Funding
11. References
ChIP-Seq Workflow
1. Introduction
2. Generate workflow environment
3. Read preprocessing
4. Alignments
5. Utilities for coverage data
6. Peak calling with MACS2
7. Annotate peaks with genomic context
8. Count reads overlapping peaks
9. Differential binding analysis
10. GO term enrichment analysis
11. Motif analysis
12. Version Information
13. Funding
14. References
VAR-Seq Workflow
1. Introduction
2. Generate workflow environment
3. Read preprocessing
4. Alignments
5. Variant calling
6. Filter variants
7. Annotate filtered variants
8. Combine annotation results among samples
9. Summary statistics of variants
10. Venn diagram of variants
11. Plot variants programmatically
12. Version Information
13. Funding
14. References
Graphics and Visualization
1. Overview
2. Base Graphics
3. Grid Graphics
4. lattice Graphics
5. ggplot2 Graphics
6. Specialty Graphics
7. Genome Graphics
8. References
Cluster Analysis
1. Introduction
2. Data Preprocessing
3. Clustering Algorithms
4. Clustering Exercises
5. Version Information
6. References
Paper Presentations
Paper Presentations
Course Projects
Overview
ChIP-Seq1 - Motif Prediction
ChIP-Seq2 - Peak Callers
RNA-Seq1 - Aligners
RNA-Seq2 - DEG Methods
VAR-Seq1 - Functional Prediction
Project Data
6. References
Hathaway, R J, Bezdek, J C, Pal, N R (1996) Sequential Competitive Learning and the Fuzzy c-Means Clustering Algorithms. Neural Netw., 9: 787–796;
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