Plant Nematode Interaction

This work is the documentation of RNA-seq data analysis to establish a gene-gene co-expression network of the Root-Knot nematode Meloidogyne incognita which forages on the roots of over 5,500 species of plants causing crop loss.
The work is licensed under The MIT License. This means that you are able to copy, share and modify the work, as long as the result is distributed under the same license.

Overview

This pipeline consists of the processing of transcriptomic data. The pipeline was developed while undertaking my postgraduate project, and it includes the following steps:

• Sourcing of Datasets from NCBI
• Checking of data quality, removal of adapters, and low-quality reads from the RNA-seq data
• Alignment to the reference genome
• Counting the coding sequences/genes present in the expression dataset
• Merging the Count files into one expression dataset
• Generate a weighted gene-gene interaction network, modules, and hub genes.
• Visualize the network, modules, and hub genes in Cytoscape.
• Map identified gene cluester to g: Profiler to determine the biological functions of the modules.

1. Data Acquistion

The datasets that were needed to run the Gene-coexpression network(GCN) analysis:
Create folders in your home directory to store data and results:

mkdir data
mkdir results

• Reference genome and annotation file:
  We acquired the reference and annoation genome of Meloidogyne incognita from the NCBI genome database and securely downloaded it using wget.

# Acquiring reference genome
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/014/132/215/GCA_014132215.1_MINJ2/GCA_014132215.1_MINJ2_genomic.fna.gz
# Acquiring annotation file
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/014/132/215/GCA_014132215.1_MINJ2/GCA_014132215.1_MINJ2_genomic.gbff.gz

gunzip *.gz # unzips all the present files.

• RNA-seq data:
  We acquired openly stored transcriptomic datasets from the SRA database stored under Accession SRP109232.
  The sra explorer was used to extract file tranfer protocols (ftp) to download our dataset securely.
  In this illustration we will download one dateset, SRR5684404, the ftp links of the forward and reverse reads:

wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR568/004/SRR5684404/SRR5684404_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR568/004/SRR5684404/SRR5684404_2.fastq.gz

• Metadata File: We extracted the metadata file to be used for this analysis from SRA Run selector. The metadata file contained detailed description of each sample file.

2. Checking quality

3. Alignment to a reference genome

We performed read alignment or mapping to ascertain where our reads came from in the genome. HISAT2, a fast and sensitive aligner was used for mapping the next-generation reads to the reference genome.
The alignment process consists of two steps:

• Indexing the reference genome
  Indexing of the reference genome was done to speed up the alignment process by enabling HISAT2 to discover potential alignment locations for query sequences.

hisat2-build /ref_genome/GCA_900182535.1_Meloidogyne_incognita_V3_genomic.fna GCA_014132215.1_MINJ2_genomic

You will see output that starts like this:

• Aligning the reads to the reference genome
  We will start aligning of reads using one of the samples in our dataset (SRR5684404). Later, we will be iterating this whole process on all sample files.
  An example of what a hisat2 command looks like is below. All index files have this as their base name GCA_900182535.1_Meloidogyne_incognita_V3_genomic. The data had pair end reads, where the forward reads are held in SRR5684404_1.fastq.gz and the reverse reads in SRR5684404_1.fastq.gz.

hisat2 -x /ref_genome/GCA_900182535.1_Meloidogyne_incognita_V3_genomic \
 -1 /data/SRR5684404_1.fastq.gz -2 /data/SRR5684404_2.fastq.gz \
 -S /results/SRR5684404.sam

You will see output that starts like this:

You can have the preview of the alignment script used to run all the samples.

4. Counting the coding sequences/genes present in the expression dataset

The count of sample SRR5684404 has the first 20 ENSEMBL_GeneIDs. count matrix

Count of each samples are merged into one sample using an R script.

You can have the preview of the count script used to generate gene counts for all samples.