Key Steps in RNASeq

1. Sample Preparation:

   • RNA Extraction: RNA is extracted from the biological sample.

   • RNA Quality Check: The quality and integrity of the RNA are assessed using methods like Bioanalyzer or gel electrophoresis.

2. Library Preparation:

   • Fragmentation: RNA is fragmented into smaller pieces.

   • cDNA Synthesis: RNA fragments are reverse transcribed into complementary DNA (cDNA).

   • Adaptor Ligation: Adapters are added to the cDNA fragments to facilitate sequencing.

   • Amplification: The cDNA is amplified by PCR to increase the quantity for sequencing.

3. Sequencing:

   • The prepared cDNA library is sequenced using high-throughput sequencing technologies (e.g., Illumina, PacBio, Oxford Nanopore).

4. Data Analysis:

   • Quality Control: Raw sequencing data is assessed for quality.

   • Read Alignment: Sequenced reads are aligned to a reference genome or transcriptome.

   • Transcript Quantification: The abundance of each transcript is measured.

   • Differential Expression Analysis: Differences in transcript levels between different conditions or samples are analyzed.

   • Functional Analysis: Pathways and gene ontologies related to differentially expressed genes are explored.

Applications of RNASeq

• Gene Expression Profiling: Identifying and quantifying gene expression levels under various conditions.

• Transcriptome Assembly: Reconstructing the transcriptome, especially useful in organisms without a reference genome.

• Alternative Splicing Analysis: Detecting and quantifying different splice variants of genes.

• Non-coding RNA Discovery: Identifying non-coding RNAs, such as miRNAs and lncRNAs.

• Mutation and SNP Detection: Identifying mutations and single nucleotide polymorphisms (SNPs) in the transcribed regions.

• Functional Genomics: Understanding the function of genes and regulatory elements in the genome.

Advantages of RNASeq

• High Throughput: Can analyze thousands of genes simultaneously.

• High Sensitivity: Detects low-abundance transcripts.

• Wide Dynamic Range: Quantifies transcripts with a wide range of expression levels.

• Unbiased: Does not require prior knowledge of the transcriptome.

Challenges of RNASeq

• Data Complexity: Produces large and complex datasets that require sophisticated computational tools for analysis.

• Cost: While costs have decreased, high-throughput sequencing can still be expensive, especially for large projects.

• Technical Variability: Variability in library preparation, sequencing, and data analysis can affect results.

Tools and Software for RNASeq Analysis

• Quality Control: FastQC, MultiQC.

• Read Alignment: HISAT2, STAR.

• Quantification: HTSeq, featureCounts.

• Differential Expression: DESeq2, edgeR, limma.

• Visualization: IGV (Integrative Genomics Viewer), ggplot2 in R.