HTS2018 resources¶
Source materials can bd cloned from the HTS2018 GitLab Repository
Unix Shell
- Duke HTS Summer Course 2018
- Introduction to Unix and bash
- Bash Scratchpad
- Hack for SIGPIPE error in Jupyter notebook
- Safety first
- Challenge
- File and directory management
- Using variables
- Downlaoding files
- Working with compressed files
- Inspecting the GTF file
- Combining operations with cat and pipe
- Filtering comment lines
- Creating new files with redirection operators
- Cutting columns from tabular data
- Sorting and counting
- Features on a chromosome - using
awk
- The Unix Shell: File and Directory Management
- The Unix Shell: Working with Text
- The Unix Shell: Finding Stuff
- The Unix Shell: Regular Expressions
- The Unix Shell: Writing Shell Scripts
- The Unix Shell: Exercises with Solutions
- Using
bashin bioinformatics - Bash Exercise (Solutions)
- Bash Exercise 2: Working with a GTF file (Solutions)
Bioinformatics
- Introduction to FASTQ Files
- What do quality scores mean?
- What do quality scores mean?
- Quality Control
- Trimming and Filtering a FASTQ
- Trimming and Filtering
- Download and Index Genome
- Mapping Reads to a Reference Genome
- Hands On Bioinformatics Exercise #1
- Counting Reads
- Working with Paired-Reads
- Making Generic Commands
- Making a Pipeline
- Working with Loops
- Working with Loops
- Looping with Globs
- Hands On Bioinformatics Exercise #2
- Prepare Data
- Downloading Everything
- Using IGV
- Run with shorter intron limit
- Hands On Bioinformatics Exercise #3
- Demultiplex a Raw FASTQ
- Raw Data
- Bash Functions
- MCT Racks
- Magnets
- Printing
Analysis of Pilot Data
- About the notebook
- About the purpose of the analysis
- Create directories
- Check all the files we need
- Set up environment
- Read in the count data output from STAR
- Arrange the results from the count files
- Store the results
- The End
- Set up environment
- Read in results
- Check the label between metadata and mapping results
- Construct gene count matrix for each library
- Metadata
- Store the results
- Visualize the mapping results
- The End
- Set Environment
- Import Data & Create DESeq from Count Data
- Inspect object & Slots of an S4 class
- Estimate Size Factors and Dispersion Parameters
- Differential Expression Analysis
- Converting/Normalizing Counts to “Expressions”
- FPM
- Regularized log transformation
- Variance Stabilizing Transformation (vst) and mean-variance modelling at the observational level (voom)
- The End
- Pilot Study: Interaction analysis with DESeq2
R Graphics and Data Manipulation
R Statistical Analysis