Bioinformatic Analysis of RNA-sequencing

By day (5 days total):

 

1. Retrieving raw data from the facility and reference data onto a lab workstation or an HPC cluster and perform mapping
2. Understand the contents of the raw and mapped files, prepare summary data to export to a laptop
3. Get R going with the proper analysis packages on each student’s laptop, start the analysis of the retrieved data
4. Finalize the analysis, plus start analysis of a selected/assigned dataset per group of students
5. Each group of students prepares a report on their assigned dataset in the morning and presents their results in the afternoon

 

The course topics will include:

Day 1: Working with data at the command line

• A refresher of bash and R scripting
• Tips and tricks for data reproducibility

Day 2: Sequencing alignments

• Downloading and aligning raw RNA-seq data on HPC clusters
• Quality assessment of sequenced and aligned files

Day 3: Bulk RNA-sequencing workflows

• Getting read counts per gene
• Data quality assessment
• Differential expression analysis and visualization
• Gene ontology analysis

Day 4: Single cell RNA-sequencing workflows

• Filtering and normalizing single cell datasets
• Cell clustering and cell type assignments
• Differential expression analysis and visualization

Day 5: Prepare and present reports of independent student analysis performed in teams

 

Learning Outcomes

By the end of this course, you should be able to:

1. retrieve raw fastq data and process them to derive gene counts
2. assess quality at several levels (raw read quality, mapping quality, sample quality)
3. perform a differential expression analysis
4. gather functional annotation and emit hypotheses on interpreting the resultsBy day (5 days total)