Soybean Transcriptome Analysis

Overview

This repository contains scripts and documentation for analyzing the transcriptome of soybean (Glycine max). This project provides insights into gene expression patterns and regulatory mechanisms in soybean under various time points (dpi1, dpi7, and dpi14).

Dataset

The dataset used for this analysis consists of RNA sequencing (RNA-seq) data obtained from soybean samples from the Biology Department, Brandon University. The data includes reads generated from different tissues, developmental stages, and experimental treatments.

Samples

• Control
• Treatment (Alternaria)

Analysis Pipeline

Data Preprocessing and Quality Control

• Read Trimming: Removed low-quality bases and adapter sequences from raw sequencing reads using (e.g., FASTQ, Trimmomatic)
• Read Alignment: Mapped trimmed reads to a reference genome or transcriptome from the NCBI or Ensembl genome database using alignment software (e.g., STAR, HISAT2).
• Read Alignment Quality Check: Sorted, indexed, and filtered mapped trimmed reads for additional quality check using software (e.g., Samtools)
• Quantification: Estimated transcript abundance as reads per kilobase of transcript per million mapped reads (RPKM) or fragments per kilobase of transcript per million mapped reads (FPKM) using (e.g., FeatureCounts)

Differential Expression Analysis

• Normalization: Normalized expression data to account for sequencing depth and transcript length biases.
• Statistical Analysis: Identified differentially expressed genes between experimental conditions.
• Visualization: Generated plots (e.g., principal component analysis, volcano plots, heatmaps) to visualize gene expression changes.

Functional Analysis

• Gene Ontology (GO) Enrichment: Determined overrepresented biological processes, molecular functions, and cellular components among differentially expressed genes using David Bioinformatics database.
• Pathway Analysis: Identified enriched biological pathways using databases such as KEGG and ShinyGO.

Validation

qPCR Validation: Validated RNA-seq results by quantitative real-time PCR (qPCR) for selected genes of interest.

Interpretation and Reporting

• Biological Interpretation: Interpreted RNA-seq results in the context of experimental objectives and biological significance.
• Report Generation: Compiled findings into research reports or manuscripts for publication.

Dependencies

• FastQC
• Trimmomatic
• STAR
• samtools
• featureCounts
• PyDESeq2
• python packages for visualization (pca, ggplot2, pheatmap, valcano)
• Python packages (pandas, numpy, matplotlib, scanpy, bioinfokit, Seaborn)