RNA sequencing Analysis - From fastq files to differential expression analysis

Overview of RNA sequencing

Installations and dependencies

# For part A - fastq to coverage
sudo apt-get install fastqc
sudo apt-get install bwa
sudo apt-get install samtools 
sudo apt-get install bedtools

#Requirements for part B - coverage files to differential gene expression will be installed in google collab

Folder contents

File/Folder Name	Description	Usage	Source
GCF_000022005.1_ASM2200v1_genomic.fna	A file with the FNA file extension is a FASTA Format DNA and Protein Sequence Alignment file that stores DNA information that can be used by molecular biology software.	A reference for alignment of the sequences from fastq files	NCBI Database
GCF_000022005.1_ASM2200v1_genomic.gff	The general feature format (gene-finding format, generic feature format, GFF) is a file format used for describing genes and other features of DNA, RNA and protein sequences.	A reference for features associated with every protein coding gene - start/stops and annotations used on the fna file	NCBI Database
Input_Output_Storage_Folder	Folder to store fastq files from sequencing runs	Storage folder for all files in analysis
gff2bed.py	Code to convert gff to bed file formats.	First step converts the gff to a readable format to generate 5 additional files in the same folder	Written by Nitish Malhotra.
RNA_Seq_Analysis_Pipeline_Paired_End.py	Code to run all the RNA sequencing steps on the fastq files for paired end sequencing	Python containing code for fastq to coverage file generation	Written by Neha Sontakke.
RNA_Seq_Analysis_Pipeline_Single_End.py	Code to run all the RNA sequencing steps on the fastq files for single ended sequencing	Python containing code for fastq to coverage file generation	Written by Neha Sontakke.
EdgeR Normalization 190423.ipynb	Code for DGE analysis and Log Fold Change Outputs	R code for coverage to logFC generation	Written by Neha Sontakke.
RPKM, TPM, FPKM, CPM calculations.ipynb	Code for Normalization Outputs	Python code for coverage to FPKM/RPKM and TPM generation	Written by Neha Sontakke.

File usage flowchart

From fastq to coverage files

#In the terminal
python RNA_Seq_Analysis_Pipeline_Paired_End.py

#or

python RNA_Seq_Analysis_Pipeline_Single_End.py

From coverage to EdgeR calculated log FC files

The list below specifies all places requiring user input

1. Open EdgeR Normalization 190423.ipynb in google collab
2. Run the first line of code
3. Upload your coverage files to the data folder
4. Specifying the names of the samples and number of files for that replicate (This is Step 6). This cell needs to be filled in with the following information: paste(rep("SAMPLENAME",NUMBER_OF_COVERAGE_FILES),seq(1,NUMBER_OF_COVERAGE_FILES),sep = ""). For example: colnames(GenewiseCounts) <- c(paste(rep("lexASidA",2),seq(1,2),sep = ""),paste(rep("WT_0",4),seq(1,4),sep = ""))_
5. List of files with the replicates named as specified (This is Step 7). Just like above this needs a specification of individual samples: rep("SAMPLENAME",NUMBER_OF_COVERAGE_FILES). For example: group<- c(rep("lexASidA",2),rep("WT_0",4))
6. Comparative groups have to be specified below. The prefix “group” has to be present, the sample names have to be given as specified in step 6 and 7 above. "groupSampleName-groupSampletobeNormalisedto". For example: comparisons<-c("grouplexASidA-groupWT_0")

** Replace gff and fna files based on organism of interest from the NCBI databases (Also replace the names inside the code in that case).

From coverage to RPKM/FPKM and TPM files

The list below specifies all places requiring user input

1. Open RPKM, TPM, FPKM, CPM calculations.ipynb in google collab
2. Run the first line of code
3. Upload your coverage files to the data folder
4. Run the rest of the code and collect your final data from RPKM_FPKM_TPM_Outputs.zip