Team Assignment - Alignment
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
- The libraries are prepared as paired end.
- The samples are sequenced on an Illumina HiSeq 2500.
- Each read is 63 bp long
- The data are RF/fr-firststrand stranded (dUTP)
- The source dataset is located here: GSE136366
- 3 samples are from TDP-43 Knockout HeLa cells and 3 samples wherein a wildtype TDP-43 transgene was re-expressed.
- For this exercise we will be using different subsets of the reads:
- Team A: chr11
- Team B: chr12
- Team C: chr17
- Team D: chr19
- Team E: chr6
- The files are named based on their SRR id’s, and obey the following key:
- SRR10045016 = KO sample 1
- SRR10045017 = KO sample 2
- SRR10045018 = KO sample 3
- SRR10045019 = Rescue sample 1
- SRR10045020 = Rescue sample 2
- SRR10045021 = Rescue sample 3
Part I - Obtaining the dataset & reference files
Goals:
- Obtain the files necessary for data processing
- Review reference and annotation file formats
- Review sequence FASTQ format
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...)
cp /home/ubuntu/workspace/team_exercise_data/data/dataset_XX.tar.gz ./dataset.tar.gz
tar -xzvf dataset.tar.gz
# Instructions below for how to download the datasets, if need be
# 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:
- Perform adapter trimming on your data and also pre-trim 5 bases from end (right) of reads
- Perform QC on your data with
fastqcandmultiqcbefore and after trimming your data
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:
- Create HISAT2 index files for your chromosome
- Review HISAT2 alignment options
- Perform alignments
- Obtain an alignment summary
- Sort and convert your alignments into compressed BAM format
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:
- Perform post-alignment QC analysis using
fastqcandmultiqc - Merge bam files (one for each condition) for easier visualization in IGV
- Index the bam files
- Explore the alignments using IGV
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).