All Study Guides Bioinformatics Unit 7
🧬 Bioinformatics Unit 7 – Gene Expression and TranscriptomicsGene 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
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