Bioinformatics

🧬Bioinformatics Unit 7 – Gene Expression and Transcriptomics

Gene expression is the process of converting genetic information into functional products like proteins. This complex process involves transcription, RNA processing, and translation, all tightly regulated to ensure proper cellular function and response to environmental stimuli. Transcriptomics studies the complete set of RNA transcripts in a cell or tissue. Using techniques like RNA-seq, it provides insights into gene expression patterns, helping researchers understand cellular processes, disease mechanisms, and potential therapeutic targets.

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


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© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.
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