RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Team Assignment - Alignment

Course

The goal of the following team assignment is for students to gain hands-on experience by working on recently published RNA-seq data and apply the concepts they have learned up to RNA alignment. To complete this assignment, students will need to review commands we performed in earlier sections.

Background on Dataset used

In this assignment, we will be using subsets of the GSE136366 dataset (Roczniak-Ferguson A, Ferguson SM. Pleiotropic requirements for human TDP-43 in the regulation of cell and organelle homeostasis. Life Sci Alliance 2019 Oct;2(5). PMID: 31527135). This dataset consists of 6 RNA sequencing files of human cells that either express or lack the TDP-43 protein.

Experimental Details

Part I - Obtaining the dataset & reference files

Goals:

As mentioned previously, we have subsetted the 6 RNA-seq samples into 5 different chromosome regions. Each team can download their corresponding dataset using the following commands.

cd $RNA_HOME/
mkdir -p team_exercise/untrimmed
cd team_exercise/untrimmed

# Fill in the "XX" below with your team's letter (A, B, C...)
wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_XX/dataset.tar.gz
tar -xzvf dataset.tar.gz

Additionally, teams will need to create a separate directory and download the corresponding reference files needed for RNA alignment & further expression analysis. Don’t forget to modify the below commands to use your team’s chromosome.

mkdir -p $RNA_HOME/team_exercise/references
cd $RNA_HOME/team_exercise/references

## Adapter trimming
wget -c http://genomedata.org/seq-tec-workshop/references/RNA/illumina_multiplex.fa

## Reference fasta corresponding to your team's assigned chromosome (e.g. chr6)
wget -c http://genomedata.org/seq-tec-workshop/references/RNA/chrXX.fa

## Obtain annotated reference gtf file corresponding to your team's assigned chromosome (e.g. chr6)
wget -c http://genomedata.org/seq-tec-workshop/references/RNA/chrXX_Homo_sapiens.GRCh38.95.gtf

Part II - Data Preprocessing (QC & Trimming)

Goals:

Q1. What is the average percentage of reads that are trimmed?

Q2. How do you expect the sequence length distribution to look prior to and after trimming? Is your answer confirmed by the multiqc report results?

Q3. Are there any metrics where the sample(s) failed?

Part III - Alignment

Goals:

A useful option to add to the end of your commands is 2>, which redirects the stderr from any command into a specific file. This can be used to redirect your stderr into a summary file, and can be used as follows: my_alignment_command 2> alignment_metrics.txt. The advantage of this is being able to view the alignment metrics later on.

Q4. What were the percentages of reads that aligned to the reference for each sample?

Q5. By compressing your sam format to bam, approximately how much space is saved (fold change in size)?

Part IV - Post-alignment QC & IGV Visualization

Goals:

Q6. How does the information from your post-alignment QC report differ from pre-alignment QC?

Q7. IGV: Can you identify certain exons that have significantly more/less coverage in one of your KO/RESCUE samples compared to the other? What is happening here?

Q8. IGV: Can you identify regions where the RNAseq reads are mapping to unexpected regions? What do you think is the reason for this phenomenon?

Q9. IGV: Can you identify a gene region that has RNA sequencing support for multiple isoforms?

Presenting Your Results

At the end of this team exercise, groups will present findings from their QC reports and IGV analysis to the class for specific questions listed below.

Team A: Present IGV findings regarding question 9.

Team B: Present multiqc report on pre- and post-alignment qc files (question 6).

Team C: Present IGV findings regarding question 7.

Team D: Present IGV findings regarding question 8.

Team E: Present multiqc report on pre- and post-trimming qc files (Data Preprocessing section).

Course