RNA structure and function are fundamental to understanding gene expression and regulation. This topic explores the diverse types of RNA molecules, their structures, and roles in cellular processes. From mRNA to non-coding RNAs, each type serves unique functions in the flow of genetic information.

RNA's complex structures, from primary sequences to quaternary assemblies, are crucial for its functions. This section delves into RNA folding principles, structure prediction methods, and the importance of RNA-protein interactions in gene expression and regulation.

Types of RNA molecules

• RNA molecules play crucial roles in various cellular processes, serving as intermediaries between DNA and proteins in gene expression
• Understanding different RNA types is fundamental to bioinformatics, as it informs the analysis of gene expression data and the development of RNA-based therapies

Messenger RNA (mRNA)

• Carries genetic information from DNA to ribosomes for protein synthesis
• Contains coding regions (exons) and non-coding regions (introns)
• Undergoes post-transcriptional modifications (5' cap, poly-A tail)
• Lifespan varies from minutes to hours, allowing for rapid regulation of gene expression

Transfer RNA (tRNA)

• Transports amino acids to ribosomes during protein synthesis
• Consists of a cloverleaf secondary structure with three loops and a stem
• Contains an anticodon loop complementary to mRNA codons
• Aminoacyl-tRNA synthetases attach specific amino acids to tRNA molecules

Ribosomal RNA (rRNA)

• Forms the structural and catalytic core of ribosomes
• Comprises about 80% of total cellular RNA
• Includes 28S, 18S, and 5.8S rRNAs in eukaryotes (23S, 16S, and 5S in prokaryotes)
• Catalyzes peptide bond formation during protein synthesis

Non-coding RNAs

• Functional RNA molecules that are not translated into proteins
• Includes microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and small nuclear RNAs (snRNAs)
• Regulate gene expression through various mechanisms (transcriptional, post-transcriptional)
• Play roles in epigenetic modifications, splicing, and cellular differentiation

RNA structure

• RNA structure is critical for its function in cellular processes and interactions with other molecules
• Bioinformatics tools and algorithms are used to predict and analyze RNA structures, aiding in the understanding of RNA function and drug design

Primary structure

• Linear sequence of nucleotides (A, U, G, C) connected by phosphodiester bonds
• Determined by the order of nucleotides transcribed from DNA
• Forms the basis for higher-order structures through base pairing and other interactions
• Can be represented as a string of letters in bioinformatics analyses

Secondary structure

• Formed by base pairing between complementary nucleotides within the RNA molecule
• Includes common motifs (hairpin loops, bulges, internal loops)
• Stabilized by hydrogen bonds between base pairs (A-U, G-C, and G-U wobble pairs)
• Predicted using algorithms based on thermodynamic principles and comparative sequence analysis

Tertiary structure

• Three-dimensional arrangement of RNA secondary structure elements
• Involves long-range interactions between distant regions of the RNA molecule
• Includes complex motifs (pseudoknots, kissing hairpins, triple helices)
• Often requires experimental techniques (X-ray crystallography, NMR) for accurate determination

Quaternary structure

• Interactions between multiple RNA molecules or RNA-protein complexes
• Forms functional units (ribosomes, spliceosomes, RISC complexes)
• Stabilized by intermolecular hydrogen bonds, electrostatic interactions, and van der Waals forces
• Studied using techniques (cryo-EM, SAXS) to understand large macromolecular assemblies

RNA folding

• RNA folding is a dynamic process crucial for the formation of functional RNA structures
• Bioinformatics approaches model RNA folding to predict structures and understand RNA-based regulation

Base pairing rules

• Watson-Crick base pairs (A-U, G-C) form strongest interactions
• Wobble base pairs (G-U) contribute to structural stability
• Non-canonical base pairs (A-A, G-A) occur in some RNA structures
• Stacking interactions between adjacent base pairs stabilize helical regions

Stem-loop structures

• Consist of a double-stranded stem and a single-stranded loop
• Common in various RNAs (tRNA, riboswitches, miRNA precursors)
• Function in RNA-protein recognition and regulatory mechanisms
• Stability depends on stem length, loop size, and sequence composition

Pseudoknots

• Form when nucleotides in a loop base pair with complementary sequences outside the loop
• Involved in ribosomal frameshifting, telomerase function, and viral RNA packaging
• Challenging to predict computationally due to their complex topology
• Detected using specialized algorithms (PknotsRG, IPknot) in RNA structure prediction

RNA thermodynamics

• Folding driven by minimization of free energy ($\Delta G = \Delta H - T\Delta S$)
• Nearest-neighbor model used to calculate energy contributions of base pairs
• Temperature affects stability of RNA structures (melting curves)
• Cofolding and cotranscriptional folding influence final RNA structure

RNA function in gene expression

• RNA molecules play diverse roles in gene expression, from carrying genetic information to regulating gene activity
• Bioinformatics tools analyze RNA sequences and structures to infer functional roles in gene expression pathways

Transcription

• RNA polymerase synthesizes RNA from DNA template
• Promoter sequences guide transcription initiation
• Transcription factors regulate gene expression levels
• Termination signals (poly-A signals, Rho-dependent) end transcription

Post-transcriptional modifications

• 5' capping protects mRNA from degradation and aids in translation initiation
• Splicing removes introns and joins exons, allowing for alternative splicing
• Polyadenylation adds poly-A tail, influencing mRNA stability and translation
• RNA editing alters nucleotide sequence (A-to-I, C-to-U conversions)

Translation

• mRNA codons specify amino acid sequence of proteins
• tRNAs deliver amino acids to growing polypeptide chain
• Ribosomes catalyze peptide bond formation
• Translation factors (initiation, elongation, termination) regulate process

Gene regulation

• Riboswitches modulate gene expression in response to metabolite binding
• miRNAs target mRNAs for degradation or translational repression
• lncRNAs regulate transcription through various mechanisms (enhancer RNAs, chromatin modifiers)
• RNA interference pathways silence genes through targeted degradation of mRNAs

RNA-seq analysis

• RNA sequencing is a powerful tool for studying gene expression and RNA populations
• Bioinformatics pipelines process and analyze RNA-seq data to gain insights into transcriptomes

Library preparation

• RNA extraction and quality assessment (RNA integrity number)
• rRNA depletion or poly-A selection to enrich for mRNAs
• Fragmentation of RNA to desired length
• cDNA synthesis and adapter ligation for sequencing

Sequencing technologies

• Short-read sequencing (Illumina) provides high throughput and accuracy
• Long-read sequencing (PacBio, Oxford Nanopore) captures full-length transcripts
• Single-cell RNA-seq reveals cell-to-cell variability in gene expression
• Direct RNA sequencing allows detection of RNA modifications

Read mapping

• Alignment of sequencing reads to reference genome or transcriptome
• Splice-aware aligners (STAR, HISAT2) handle spliced reads
• De novo transcriptome assembly for non-model organisms
• Quantification of gene and transcript expression levels (FPKM, TPM)

Differential expression analysis

• Statistical methods (DESeq2, edgeR) identify differentially expressed genes
• Normalization techniques account for sequencing depth and composition biases
• Multiple testing correction controls for false discoveries
• Visualization tools (heatmaps, volcano plots) aid in interpreting results

RNA structure prediction

• Computational prediction of RNA structures is essential for understanding RNA function
• Bioinformatics algorithms combine thermodynamic models and comparative analyses to predict RNA structures

Minimum free energy models

• Dynamic programming algorithms (Zuker algorithm) find optimal secondary structure
• Nearest-neighbor thermodynamic parameters used to calculate folding energy
• Suboptimal structure prediction explores alternative conformations
• Incorporation of experimental constraints improves prediction accuracy

Comparative sequence analysis

• Utilizes evolutionary conservation of RNA structures across species
• Covariation analysis identifies compensatory mutations maintaining base pairs
• Multiple sequence alignments guide structure prediction
• Consensus structure prediction combines individual predictions across homologs

Machine learning approaches

• Deep learning models (SPOT-RNA, E2Efold) predict secondary structures from sequence
• Feature extraction from known RNA structures informs prediction algorithms
• Integration of experimental data (SHAPE-seq, DMS-seq) improves predictions
• Ensemble methods combine multiple prediction approaches for increased accuracy

RNA-protein interactions

• RNA-protein interactions are crucial for many cellular processes and gene regulation
• Bioinformatics tools predict and analyze RNA-protein binding sites and complexes

RNA-binding proteins

• Recognize specific RNA sequences or structural motifs
• Contain RNA-binding domains (RRM, KH, zinc finger)
• Regulate RNA processing, localization, and stability
• Examples include splicing factors (SRSF1), translation factors (eIF4E), and RNA helicases (DDX5)

Ribonucleoproteins

• Complexes of RNA and proteins with specific cellular functions
• Include ribosomes, spliceosomes, and telomerase
• Formation often involves stepwise assembly of multiple components
• Structural studies (cryo-EM) reveal intricate architectures of RNPs

CLIP-seq techniques

• Cross-linking immunoprecipitation followed by sequencing
• Identifies transcriptome-wide binding sites of RNA-binding proteins
• Variants include HITS-CLIP, PAR-CLIP, and iCLIP
• Bioinformatics analysis involves peak calling and motif discovery algorithms

RNA editing and modification

• RNA editing and modifications alter RNA sequences and structures post-transcriptionally
• Bioinformatics approaches detect and analyze RNA modifications from sequencing data

A-to-I editing

• Adenosine deaminases (ADARs) convert adenosine to inosine
• Occurs primarily in double-stranded RNA regions
• Affects mRNA coding potential, splicing, and miRNA targeting
• Bioinformatics tools (REDItools, JACUSA) identify A-to-I editing sites from RNA-seq data

C-to-U editing

• Cytidine deaminases (APOBECs) convert cytidine to uridine
• Examples include APOBEC1-mediated editing of apolipoprotein B mRNA
• Can create or eliminate stop codons, altering protein sequences
• Computational methods compare DNA and RNA sequences to detect C-to-U editing events

RNA methylation

• Common modifications include m6A, m5C, and 2'-O-methylation
• Affects RNA stability, localization, and translation efficiency
• Detected using antibody-based enrichment (MeRIP-seq) or direct sequencing (Nanopore)
• Bioinformatics tools (MACS2, METEORE) identify methylation sites from sequencing data

RNA interference

• RNA interference is a conserved mechanism for gene silencing
• Bioinformatics tools predict miRNA targets and analyze RNAi pathways

siRNA vs miRNA

• siRNAs derived from long double-stranded RNA precursors
• miRNAs originate from hairpin structures in primary miRNA transcripts
• siRNAs typically have perfect complementarity to targets
• miRNAs often have partial complementarity, primarily in the seed region

RISC complex

• RNA-induced silencing complex mediates gene silencing
• Core components include Argonaute proteins and guide RNA (siRNA or miRNA)
• Assembly involves loading of guide RNA and passenger strand removal
• Structure and function studied using biochemical and structural biology approaches

Gene silencing mechanisms

• mRNA cleavage by Argonaute proteins (perfect complementarity)
• Translational repression and mRNA destabilization (partial complementarity)
• Transcriptional gene silencing through chromatin modifications
• Amplification of silencing signal in some organisms (RNA-dependent RNA polymerases)

Ribozymes and catalytic RNAs

• Catalytic RNAs demonstrate the diverse functional capabilities of RNA molecules
• Bioinformatics approaches identify and characterize ribozymes in genomic sequences

Self-splicing introns

• Group I and Group II introns catalyze their own excision from precursor RNAs
• Utilize different catalytic mechanisms (external guanosine cofactor vs internal nucleophile)
• Found in various organisms (bacteria, fungi, plants) and organellar genomes
• Computational tools (RNAweasel, INFERNAL) detect self-splicing introns in genomic sequences

Riboswitches

• RNA elements that regulate gene expression through conformational changes
• Bind specific metabolites or ions (thiamine pyrophosphate, S-adenosylmethionine)
• Modulate transcription termination or translation initiation
• Bioinformatics approaches (Rfam database, CMfinder) identify riboswitch motifs in genomes

Therapeutic applications

• Ribozymes engineered for targeted RNA cleavage (hammerhead, hairpin ribozymes)
• Potential applications in gene therapy and antiviral treatments
• CRISPR-Cas systems adapted for RNA targeting and editing
• Computational design tools optimize ribozyme sequences for specific targets

RNA in evolution

• RNA plays a central role in theories of early life and molecular evolution
• Bioinformatics approaches analyze RNA sequences and structures to infer evolutionary relationships

RNA world hypothesis

• Proposes RNA as both genetic material and catalytic molecule in early life
• Supported by discovery of ribozymes and RNA's central role in gene expression
• Challenges include RNA stability and limited catalytic repertoire
• Computational models simulate prebiotic RNA evolution and replication

Molecular fossils

• Conserved RNA structures and sequences provide insights into ancient life
• Examples include ribosomal RNA, tRNA, and RNase P RNA
• Comparative genomics reveals evolutionary history of RNA genes
• Phylogenetic analysis of RNA sequences infers relationships between organisms

Comparative genomics

• Analysis of RNA genes and regulatory elements across species
• Identification of conserved RNA structures suggests functional importance
• Synteny analysis reveals genomic rearrangements affecting RNA genes
• Evolutionary rates of RNA sequences inform functional constraints

Bioinformatics tools for RNA analysis

• Computational tools are essential for analyzing and interpreting RNA data
• Bioinformatics approaches integrate multiple data types to understand RNA function

Secondary structure visualization

• Tools (VARNA, RNAstructure) generate 2D representations of RNA structures
• Interactive visualizations allow exploration of structural features
• Color-coding highlights base-pairing probabilities and evolutionary conservation
• Integration with experimental data (SHAPE reactivity) improves structure representations

Motif discovery algorithms

• Identify recurring sequence or structural patterns in RNA molecules
• Methods include sequence-based (MEME) and structure-based (CMfinder) approaches
• Incorporate conservation information from multiple sequence alignments
• Applications in regulatory element prediction and RNA family classification

RNA-RNA interaction prediction

• Algorithms (IntaRNA, RNAup) predict base-pairing between RNA molecules
• Consider both intermolecular and intramolecular base-pairing
• Applications in miRNA target prediction and antisense RNA design
• Integration of experimental data (CLASH, PARIS) improves interaction predictions