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Setup

Setting Up The Analysis Directory

To begin we will need a space to work, let's create a directory to house all of our input data, scripts and results:

mkdir rna_seq_pipeline
cd rna_seq_pipeline

Now let's make subfolders for our data, scripts and results:

mkdir data
mkdir tools
mkdir qc_output
mkdir trimmed_output
mkdir star_index
mkdir alignment_output
mkdir featurecounts_output

Creating A Conda Environment

For reproducible research it is advisable to keep the software versions you use consistent. An easy way of ensuring this is by creating a Conda environment. For more information on how to build conda environments check out:

Conda Environments

Here, we will enter our tools directory and create a conda environment from the following yml file:

cd tools
wget https://raw.githubusercontent.com/BioNomad/omicsTrain/main/docs/omics/transcriptomics/bulk_rna_seq/data/rnaseq_environment.yml

Now, let's create the environment and activate it!

conda env create -f rnaseq_environment.yml    # create conda environment
source activate rnaseq                        # activate conda environment
cd ..                                         # leave tools directory

Downloading Fastq Read Data

Today we will be working with data from Srinivasan et al. 2020 where they assessed transcriptional changes in patients with and without Alzheimer's disease. Let's create an accession list to download a few files from this study:

cd data
nano accList.txt

accList.txt

SRR8440545
SRR8440550
SRR8440537
SRR8440481

Now we will need meta data for these samples. The following data was taken from the SRA Run Selector. SRA, or sequence read archive, is a public repository for sequence data which we are pulling from for this analysis.

nano meta.txt

meta.txt

ID  Diagnosis   Age Sex
SRR8440545  Control 53  male
SRR8440550  Control 81  male
SRR8440537  AD  74  female
SRR8440481  AD  79  male

Before we can download our data we will need to configure the sra-toolkit with the following command:

vdb-config -i

Click X and you are finished configuring! To download the sequence data we will use the following command:

fastq-dump -N 100000 -X 200000  --skip-technical --split-3 --clip --gzip  $(<./accList.txt)

Explanation of Terms

  • -N start at read 100000
  • -X end at read 200000
  • --skip-technical skip technical reads and only download biological reads
  • --split-3 split paired end data
  • --clip remove adapters
  • --gzip compress the output

Download Reference Data

Now to actually figure out what genes are expressed we need some sort of reference to map our reads too. We will be using the following reference:

Homo sapiens chromosome 17, GRCh38.p14 Primary Assembly

To download the fasta file complete the following steps:

  1. Go to Homo sapiens chromosome 17, GRCh38.p14 Primary Assembly
  2. Click Send To
  3. Click File
  4. Change the format to FASTA
  5. Click Create File
  6. Move the downloaded file to the rna_seq_pipeline/data folder

Downloading the Reference FASTA File

To download the gff3 file complete the following steps:

  1. Go to Homo sapiens chromosome 17, GRCh38.p14 Primary Assembly
  2. Click Send To
  3. Click File
  4. Change the format to GFF3
  5. Click Create File
  6. Move the downloaded file to the rna_seq_pipeline/data folder

Downloading the Reference GFF3 File

References

  1. Alzheimer’s Patient Microglia Exhibit Enhanced Aging and Unique Transcriptional Activation
  2. Homo sapiens chromosome 17, GRCh38.p14 Primary Assembly