RNAseq Analysis for Gene Expression

PRESENTED BY [DR MUHAMMAD SHAFIQ]

Introduction to RNAseq

FOUNDATIONS AND IMPORTANCE

RNA sequencing (RNAseq) measures gene expression levels, offering insights into plant growth, stress responses, and development. This technique revolutionized plant molecular biology, enabling crop improvement and understanding environmental responses. Visual aids help illustrate the gene expression process.

MEASURING GENE EXPRESSION LEVELS

RNAseq is a powerful technique that enables accurate measurement of gene expression levels in plants, unlocking insights into their functionality.

GENE EXPRESSION BASICS

Understanding gene expression is crucial for comprehending plant growth, stress responses, and developmental processes that dictate overall plant health.

REVOLUTIONIZING RESEARCH

RNAseq has transformed plant molecular biology by providing new avenues for crop improvement and enhanced knowledge of plant environmental responses.

ENHANCING AGRICULTURAL YIELDS

RNAseq technology allows precise identification of beneficial traits in crops, aiding in breeding programs aimed at improving yield and sustainability.

RNAseq Applications in Crop Improvement

RNAseq Experimental Design Overview

PLANNING RNASEQ DATA

• Importance of sample selection
• Biological replicates enhance statistical power
• RNA extraction methods for plant tissues
• Steps for library preparation
• Common sequencing platforms used

Overview of Sequencing Platforms in RNAseq

Illumina sequencing platforms are widely used for RNAseq, providing high throughput and accuracy for gene expression analysis in plants.

Data Processing in RNAseq

ENSURING QUALITY CONTROL

• Assess raw data quality with FastQC
• Use common quality metrics for evaluation
• Trim and filter reads to remove low-quality sequences
• Align reads to reference genome
• Quantify gene expression levels (counts, TPM, FPKM)

Raw Data Quality Assessment

UNDERSTANDING COMMON QUALITY METRICS IN FASTQC

• Per base sequence quality scores
• Sequence length distribution across reads
• GC content analysis for biases
• Adapter content detection in sequences
• Per sequence quality scores overview

READ TRIMMING

Read trimming is essential to remove low-quality sequences and adapter contamination, ensuring that only high-quality reads are used for downstream analysis of gene expression.

QUANTIFICATION

Quantification of gene expression levels is achieved through methods like counts, TPM, or FPKM, providing valuable insights into the activity of specific genes under various conditions.

ALIGNMENT

Alignment of trimmed reads to a reference plant genome enables accurate mapping of sequences, facilitating the identification of expressed genes and their respective expression levels.

Identification and Analysis of DEGs

STATISTICAL ANALYSIS OVERVIEW

• Define DEGs and their biological significance in plant studies.
• Overview of statistical tools: DESeq2 and edgeR.
• Visualization methods include volcano plots and heatmaps.
• Discuss challenges like batch effects.

DEGS

Differentially Expressed Genes (DEGs) are crucial for understanding biological responses in plants, especially under varying environmental conditions and developmental stages.

CRITERIA

Key criteria such as fold-change, p-value, and FDR help assess the reliability of DEG findings, ensuring that results are both biologically and statistically meaningful.

TOOLS

Statistical tools like DESeq2 and edgeR facilitate the analysis of gene expression data, allowing researchers to identify significant changes between experimental groups.

VOLCANO

Displays significance and magnitude of DEGs visually.

HEATMAP

Shows expression patterns across multiple samples clearly.

MA

Illustrates log fold-change versus mean expression levels.

Analyzing Differentially Expressed Genes in Plants

This section discusses how differentially expressed genes reveal insights into plant stress responses and developmental adaptations to changing environments.

From DEGs to Biological Insights

FUTURE DIRECTIONS IN RNASEQ

• Functional annotation of DEGs: Gene Ontology, pathway enrichment analyses relevant to plants
• Integrating DEG results with phenotypic data
• Summary of RNAseq’s impact on plant biology research
• Suggested next steps: additional omics integration, validation experiments (qPCR)

FUNCTIONAL ANNOTATION

Functional annotation of DEGs provides insights into gene functions and biological pathways, facilitating understanding of plant responses to varying environmental conditions and stressors.

INTEGRATING RESULTS

Integrating DEG results with phenotypic data aids in hypothesis generation, allowing researchers to correlate genetic changes with observable traits, ultimately enhancing crop improvement strategies.

RNASEQ IMPACT

RNAseq has significantly influenced plant biology research, enabling more precise gene expression analysis, leading to breakthroughs in genetic understanding and advancements in plant breeding programs.

SUGGESTED NEXT STEPS

Future directions include additional omics integration, validation experiments like qPCR, and exploring RNAseq tools, empowering researchers to further unlock plant gene functions and improve agricultural practices.

EMAIL CONTACT

ifo@plantmol.com

SOCIAL MEDIA

Molecular Plant Science

PHONE

03006561997