🧬Computational Genomics Unit 11 – Genomic Data Visualization & Analysis

Key Concepts and Terminology

• Genomics focuses on the structure, function, evolution, and mapping of genomes
• Bioinformatics combines biology, computer science, and statistics to analyze and interpret biological data
• Sequencing technologies (Illumina, PacBio) enable reading the nucleotide sequence of DNA or RNA
• Alignment maps sequencing reads to a reference genome to identify their genomic positions
• Variant calling detects differences (SNPs, indels, CNVs) between sequencing data and a reference genome
• Gene expression quantifies the abundance of RNA transcripts to study gene activity across conditions
• Epigenetics investigates heritable changes in gene expression not caused by alterations in the DNA sequence (DNA methylation, histone modifications)
• Functional annotation assigns biological functions to genomic elements based on experimental evidence or computational predictions

Data Types and Formats in Genomics

• FASTA format represents nucleotide or amino acid sequences using single-letter codes preceded by a header line
• FASTQ format stores both biological sequences and their corresponding quality scores, with each entry consisting of four lines
• SAM (Sequence Alignment/Map) format stores alignment information of sequencing reads mapped to a reference genome
  • BAM (Binary Alignment/Map) is the binary, compressed version of SAM for efficient storage and processing
  • CRAM is a more compressed alternative to BAM, achieving better compression ratios
• VCF (Variant Call Format) represents genetic variations (SNPs, indels, structural variants) across multiple samples
• BED (Browser Extensible Data) format defines genomic regions or features using tab-delimited fields (chromosome, start, end, name, score, strand)
• GFF (General Feature Format) and GTF (Gene Transfer Format) describe gene structures and annotations using tab-delimited fields
• BigWig and BigBed are binary, indexed formats for efficient visualization and querying of continuous and discrete genomic data, respectively

Visualization Tools and Techniques

• Genome browsers (UCSC Genome Browser, Ensembl, IGV) enable interactive exploration of genomic data by displaying various data tracks aligned to a reference genome
• Heatmaps visualize patterns and relationships in genomic data matrices, with rows representing features (genes, samples) and columns representing conditions or samples, and colors indicating values
• Principal Component Analysis (PCA) plots reduce high-dimensional genomic data to two or three dimensions, capturing the most significant sources of variation
• Volcano plots combine statistical significance ($-log_{10}(p-value)$) and magnitude of change (log fold change) to identify differentially expressed genes or regions
• Circos plots depict genomic rearrangements, chromatin interactions, or other genomic relationships in a circular layout
• Network diagrams represent interactions or functional relationships between genes, proteins, or other biological entities
• Pathway maps illustrate the involvement of genes or proteins in biological processes or signaling cascades
• Track-based visualizations (coverage plots, read alignments) help assess data quality, identify genomic features, and detect patterns or anomalies

Statistical Methods for Genomic Analysis

• Hypothesis testing evaluates the statistical significance of observed differences or associations using p-values
  • Multiple testing correction (Bonferroni, FDR) adjusts p-values to control false positives when conducting numerous tests simultaneously
• Differential expression analysis identifies genes with significant changes in expression levels between conditions using methods like DESeq2 or edgeR
• Enrichment analysis assesses the overrepresentation of functional categories or pathways among a set of genes using tools like GSEA or DAVID
• Clustering algorithms (hierarchical, k-means) group similar samples or genes based on their genomic profiles to discover patterns or subtypes
• Machine learning techniques (classification, regression) build predictive models from genomic features to infer biological outcomes or traits
• Survival analysis investigates the relationship between genomic variables and time-to-event outcomes using methods like Kaplan-Meier curves or Cox proportional hazards models
• Bayesian inference incorporates prior knowledge and updates beliefs based on observed data to estimate posterior probabilities of genomic events or parameters

Data Preprocessing and Quality Control

• Raw data processing converts sequencing machine outputs (BCL files) into readable formats (FASTQ) and performs initial quality checks
• Quality assessment tools (FastQC, MultiQC) generate reports on sequencing data quality metrics (base quality scores, GC content, duplication rates)
• Adapter trimming removes adapter sequences from reads to avoid alignment artifacts and improve mapping accuracy
• Quality filtering removes low-quality reads or bases to enhance downstream analysis reliability
• Contamination detection identifies and removes reads originating from non-target organisms (bacteria, viruses) to avoid biases
• Read deduplication removes PCR duplicates to mitigate amplification biases and improve quantification accuracy
• Batch effect correction normalizes data to minimize technical variations across samples or experiments
• Data normalization scales raw read counts to account for differences in library sizes, sequencing depths, or other systematic biases

Exploratory Data Analysis in Genomics

• Data visualization techniques (PCA, t-SNE, UMAP) help identify patterns, outliers, or batch effects in high-dimensional genomic datasets
• Sample clustering groups samples based on their genomic profiles to discover subpopulations or disease subtypes
• Correlation analysis assesses the strength and direction of relationships between genomic features or samples
• Dimensionality reduction methods (PCA, NMF) extract key features or components that capture the most relevant information in the data
• Unsupervised learning algorithms (clustering, anomaly detection) explore data structure and identify novel patterns without prior labels
• Annotation enrichment analysis identifies overrepresented functional categories, pathways, or motifs in a set of genomic features
• Integrative analysis combines multiple data types (gene expression, epigenetics, proteomics) to gain a more comprehensive understanding of biological systems

Advanced Analysis Techniques

• Deep learning models (CNNs, RNNs) learn complex patterns and representations from genomic sequences or features for tasks like variant calling, gene expression prediction, or disease classification
• Graph-based methods represent genomic data as networks to study relationships, interactions, or community structures
• Topological data analysis captures higher-order interactions and structures in genomic datasets using techniques like persistent homology
• Causal inference methods (Mendelian randomization, mediation analysis) infer causal relationships between genomic variables and phenotypes
• Spatial transcriptomics combines gene expression profiling with spatial information to study tissue heterogeneity and cellular interactions
• Single-cell genomics analyzes individual cells to uncover cellular diversity, lineage relationships, and rare cell types
• Metagenomics studies the collective genomes of microbial communities to understand their composition, function, and interactions with the environment or host
• Multi-omics integration combines data from different molecular levels (genome, transcriptome, proteome, metabolome) to obtain a systems-level understanding of biological processes

Practical Applications and Case Studies

• Cancer genomics identifies driver mutations, molecular subtypes, and therapeutic targets by analyzing tumor genomes and transcriptomes
• Precision medicine tailors treatments to individual patients based on their genomic profiles and other molecular characteristics
• Genetic association studies (GWAS) identify genetic variants associated with complex traits or diseases by comparing allele frequencies between cases and controls
• Pharmacogenomics investigates how genetic variations influence drug response and guides personalized medication choices
• Agricultural genomics applies genomic techniques to improve crop yields, resistance to stresses, and nutritional quality
• Evolutionary genomics studies the evolution of genomes across species to understand the mechanisms of adaptation, speciation, and phylogenetic relationships
• Forensic genomics uses DNA evidence to identify individuals, establish kinship, or solve crimes
• Microbiome analysis characterizes the composition and function of microbial communities in different environments (gut, soil, water) and their impact on health or ecosystem processes