Bioinformatics Unit 7 Review: Gene Expression and Transcriptomics

Key Concepts in Gene Expression

• Gene expression process by which genetic information encoded in DNA is converted into functional gene products (proteins or non-coding RNAs)
• Involves multiple steps including transcription, RNA processing, translation, and post-translational modifications
• Tightly regulated at various levels to ensure proper cellular function and response to environmental stimuli
• Differential gene expression underlies cell differentiation, development, and adaptations to changing conditions
• Dysregulation of gene expression implicated in various diseases (cancer, genetic disorders)
• Studying gene expression patterns provides insights into cellular processes, disease mechanisms, and potential therapeutic targets
• Techniques for measuring gene expression include RNA-seq, microarrays, and quantitative PCR (qPCR)

DNA Transcription and RNA Processing

• Transcription initial step of gene expression where DNA is copied into complementary RNA molecules by RNA polymerase enzymes
  • Initiated at promoter regions upstream of genes recognized by transcription factors and RNA polymerase
  • Produces precursor mRNA (pre-mRNA) that undergoes further processing
• RNA processing involves modifications to pre-mRNA before translation
  • 5' capping addition of 7-methylguanosine cap to 5' end protects mRNA and facilitates translation initiation
  • 3' polyadenylation addition of poly(A) tail to 3' end enhances mRNA stability and translation efficiency
  • Splicing removal of intronic sequences and joining of exonic sequences to produce mature mRNA
    • Alternative splicing generates mRNA isoforms with different combinations of exons, increasing proteome diversity
• Mature mRNA exported from nucleus to cytoplasm for translation into proteins
• Non-coding RNAs (ncRNAs) also produced by transcription play regulatory roles (miRNAs, lncRNAs)

Regulation of Gene Expression

• Gene expression tightly regulated to ensure proper cellular function and response to stimuli
• Transcriptional regulation controls initiation and rate of transcription
  • Transcription factors bind specific DNA sequences (enhancers, silencers) to activate or repress transcription
  • Chromatin structure and epigenetic modifications (DNA methylation, histone modifications) influence gene accessibility and transcription
• Post-transcriptional regulation controls mRNA stability, localization, and translation efficiency
  • RNA-binding proteins (RBPs) and microRNAs (miRNAs) bind mRNA to regulate stability and translation
  • Alternative splicing generates mRNA isoforms with different functions or stability
• Translational regulation controls rate and efficiency of protein synthesis
  • Ribosome binding, initiation factors, and mRNA structure influence translation initiation
• Post-translational modifications (phosphorylation, glycosylation) alter protein function, stability, and localization
• Feedback loops and regulatory networks allow precise control and coordination of gene expression in response to cellular needs

Introduction to Transcriptomics

• Transcriptomics study of the complete set of RNA transcripts (transcriptome) in a cell or tissue under specific conditions
• Provides a snapshot of gene expression at a given time point
• Allows identification of differentially expressed genes, alternative splicing events, and non-coding RNAs
• Enables understanding of cellular processes, disease mechanisms, and biomarker discovery
• Techniques for transcriptome profiling include RNA-seq, microarrays, and single-cell RNA-seq (scRNA-seq)
  • RNA-seq high-throughput sequencing of cDNA libraries derived from RNA samples
  • Microarrays hybridization-based method using oligonucleotide probes to measure transcript levels
  • scRNA-seq captures gene expression profiles of individual cells, revealing cellular heterogeneity and rare cell types
• Transcriptomic data analyzed using bioinformatics tools and statistical methods to identify patterns and derive biological insights

RNA-Seq Technology and Methods

• RNA-seq high-throughput sequencing method for transcriptome profiling
• Involves conversion of RNA to cDNA, library preparation, and massively parallel sequencing
• RNA extraction and quality assessment critical for obtaining high-quality data
  • Total RNA or specific RNA fractions (mRNA, small RNAs) isolated depending on research question
  • RNA integrity and purity assessed using electrophoresis or spectrophotometry
• Library preparation involves fragmentation, reverse transcription, adapter ligation, and amplification
  • Fragmentation generates smaller RNA pieces suitable for sequencing
  • Reverse transcription converts RNA to cDNA using random primers or oligo(dT) primers
  • Adapters ligated to cDNA fragments enable binding to sequencing flow cell and sample multiplexing
  • Amplification increases cDNA quantity for sequencing
• Sequencing performed using platforms such as Illumina, Pacific Biosciences, or Oxford Nanopore
  • Illumina most widely used, generates short reads (100-300 bp) with high accuracy
  • Long-read sequencing (PacBio, Nanopore) captures full-length transcripts and helps resolve complex isoforms
• Sequencing depth (number of reads per sample) and biological replicates important for statistical power and reproducibility

Transcriptome Data Analysis Techniques

• Raw RNA-seq data undergoes quality control, preprocessing, and alignment to reference genome or transcriptome
  • Quality control assesses read quality, identifies adapters and contaminants, and filters low-quality reads
  • Preprocessing steps include adapter trimming and read filtering
  • Alignment maps reads to reference genome or transcriptome using splice-aware algorithms (STAR, HISAT2)
• Quantification estimates transcript or gene expression levels based on read counts
  • Tools like featureCounts, HTSeq, and Salmon used for quantification
  • Normalization methods (RPKM, TPM) account for differences in library size and gene length
• Differential expression analysis identifies genes with significant changes in expression between conditions
  • Statistical methods (DESeq2, edgeR) model read counts and test for significant differences
  • False discovery rate (FDR) correction applied to adjust for multiple testing
• Alternative splicing analysis detects differential usage of exons or isoforms across conditions
  • Tools like rMATS, DEXSeq, and LeafCutter used for splicing analysis
• Functional annotation and pathway analysis interpret biological significance of differentially expressed genes
  • Gene Ontology (GO) and KEGG pathway databases used for functional enrichment analysis
  • Tools like DAVID, GSEA, and IPA integrate expression data with biological knowledge

Bioinformatics Tools for Gene Expression Studies

• Various bioinformatics tools and platforms available for RNA-seq data analysis and interpretation
• Quality control and preprocessing tools
  • FastQC assesses read quality and identifies potential issues
  • Trimmomatic and Cutadapt trim adapters and low-quality bases
• Alignment and quantification tools
  • STAR and HISAT2 fast and accurate splice-aware aligners
  • featureCounts and HTSeq count reads mapped to genomic features (genes, exons)
  • Salmon and Kallisto alignment-free quantification tools using pseudo-alignment
• Differential expression analysis tools
  • DESeq2 and edgeR widely used R packages for differential expression analysis
  • limma and voom R packages for differential expression analysis of microarray and RNA-seq data
• Visualization and interpretation tools
  • Integrative Genomics Viewer (IGV) visualizes read alignments and splicing events
  • R packages (ggplot2, pheatmap) and web-based tools (Plotly, Shiny) for interactive data visualization
  • Functional annotation and pathway analysis tools (DAVID, GSEA, IPA) integrate expression data with biological knowledge
• Workflow management systems (Galaxy, Snakemake, Nextflow) facilitate reproducible and scalable analysis pipelines

Applications and Case Studies

• Transcriptomics widely applied in various fields of biology and medicine
• Disease biomarker discovery
  • Identifying differentially expressed genes as potential diagnostic or prognostic biomarkers
  • Example: RNA-seq analysis of lung cancer subtypes identified novel biomarkers and therapeutic targets
• Drug discovery and development
  • Assessing gene expression changes in response to drug treatments
  • Example: Transcriptomic profiling of drug-resistant cancer cells revealed mechanisms of resistance and potential combination therapies
• Developmental biology and cell differentiation
  • Studying gene expression dynamics during embryonic development and cell differentiation
  • Example: Single-cell RNA-seq analysis of human embryonic stem cells uncovered distinct lineage-specific transcriptional programs
• Plant and agricultural research
  • Investigating gene expression in response to environmental stresses, pathogens, or agricultural traits
  • Example: RNA-seq analysis of drought-tolerant and sensitive rice varieties identified genes and pathways involved in drought response
• Microbiology and infectious diseases
  • Profiling gene expression of pathogens and host-pathogen interactions
  • Example: Dual RNA-seq of Mycobacterium tuberculosis and infected macrophages revealed complex interplay and potential drug targets
• Personalized medicine and diagnostics
  • Developing gene expression-based classifiers for patient stratification and treatment selection
  • Example: RNA-seq-based classifier for predicting response to immunotherapy in melanoma patients