My thoughts and ideas
My thoughts and ideas
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.
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.
mkdir -p $RNA_HOME/team_exercise/data cd $RNA_HOME/team_exercise/data ### TEAM A wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_A/dataset.tar.gz tar -xzvf dataset.tar.gz ### TEAM B wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_B/dataset.tar.gz tar -xzvf dataset.tar.gz ### TEAM C wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_C/dataset.tar.gz tar -xzvf dataset.tar.gz ### TEAM D wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_D/dataset.tar.gz tar -xzvf dataset.tar.gz ### TEAM E wget -c http://genomedata.org/seq-tec-workshop/read_data/rna_alignment-de_exercise/dataset_E/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:
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/chr6.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/chr6_Homo_sapiens.GRCh38.95.gtf
Prior to aligning RNA-seq data, teams should perform adapter trimming using
flexbar. Once the team has both the pre-trim and post-trim data, QC metrics should be evaluated using
fastqc and a general report can be generated using
<L1> Q1. What is the average percentage of reads that are trimmed?
<L2> Q2. Before looking at the multiqc report, how do you expect the sequence length distribution to look both prior to and after trimming? Is your answer confirmed by the multiqc report results?
<L1> Q3. Are there any metrics where the sample(s) failed?
A useful option to add to the end of your commands is
2>, which redirects the stdout from any command into a specific file. This can be used to redirect your stdout into a summary file, and can be used as follows:
My_alignment_script 2> alignment_metrics.txt. The advantage of this is being able to view the alignment metrics later on.
<L1> Q4. What were the percentages of reads that aligned to the reference for each sample?
<L1> Q5. By compressing your sam format to bam, approximately how much space is saved (fold change in size)?
In order to make visualization easier, we’re going to merge each of our bams into one using the following commands. Make sure to index these bams afterwards to be able to view them in IGV.
# Merge the bams for visualization purposes cd <path to dir with alignments> java -Xmx16g -jar $PICARD MergeSamFiles OUTPUT=KO_merged.bam INPUT=SRR10045016.bam INPUT=SRR10045017.bam INPUT=SRR10045018.bam java -Xmx16g -jar $PICARD MergeSamFiles OUTPUT=RESCUE_merged.bam INPUT=SRR10045019.bam INPUT=SRR10045020.bam INPUT=SRR10045021.bam
<L2> Q6. How does the information from your post-alignment QC report differ from pre-alignment QC?
<L3> Q7. IGV: Can you identify certain exons that have significantly more/less coverage in one of your KO/RESCUE samples compared to the other? What is happening here?
<L3> Q8. IGV: Can you identify regions where the RNAseq reads are mapping to unexpected regions? What do you think is the reason for this phenomenon?
<L3> Q9. IGV: Can you identify a gene region that has RNA sequencing support for multiple isoforms?
Upon obtaining the different reference files, explore the annotated reference gtf file and answer the following questions using your choice of commands.
Hint: useful commands include
<L4> Q10. What are the different types of features contained in the gtf file (e.g. transcript, gene)? What are the frequencies of the different types of features? (This is referring to the third field/column of the data).
In order to get this answer, there are a series of commands that we can pipe together:
cat <YOUR GTF FILE> | grep gene_name | cut -d$'\t' -f3 | sort | uniq -c | sort -r
Can you explain how this command works to one of the TAs?
<L5> Q11. Now that you have seen the example in Q1, can you construct a similar command that answers the questions: Which genes have the highest number of transcripts (either gene id or gene name)? How many?
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).