🧬Computational Genomics Unit 10 – Metagenomics & Microbiome Analysis

Key Concepts and Definitions

• Metagenomics involves studying the collective genomes of microorganisms in an environmental sample
• Microbiome refers to the entire community of microbes within a specific environment (gut, soil, ocean)
• Amplicon sequencing targets specific genetic markers (16S rRNA gene) to identify microbial taxa present
• Shotgun metagenomics sequences all DNA in a sample provides insights into microbial functions and interactions
• Alpha diversity measures the richness and evenness of microbial communities within a single sample
  • Richness refers to the number of unique taxa present
  • Evenness describes how evenly the taxa are distributed
• Beta diversity assesses the differences in microbial composition between samples or environments
• Operational Taxonomic Units (OTUs) cluster sequences based on similarity thresholds (97%) to define microbial taxa
• Amplicon Sequence Variants (ASVs) offer higher resolution than OTUs by distinguishing sequences differing by a single nucleotide

Microbiome Sampling Techniques

• Sample collection methods vary depending on the environment (swabs, fecal samples, water filters)
• Aseptic techniques prevent contamination during sample collection and processing
• Sample storage conditions (temperature, preservatives) maintain DNA integrity for downstream analysis
• Negative controls assess potential contamination introduced during sample processing
• Metadata collection (sample type, location, host information) provides context for data interpretation
• DNA extraction protocols optimize yield and purity while minimizing bias
  • Mechanical lysis (bead beating) disrupts tough microbial cell walls
  • Enzymatic lysis (lysozyme) digests cell wall components
• Quality control steps (gel electrophoresis, spectrophotometry) evaluate DNA quantity and purity before sequencing

DNA Sequencing for Metagenomics

• High-throughput sequencing technologies (Illumina, PacBio, Oxford Nanopore) generate millions of reads per sample
• 16S rRNA gene sequencing targets conserved and variable regions to identify bacteria and archaea
  • V3-V4 regions are commonly used for their taxonomic resolution
  • Primers designed to minimize amplification bias and maximize coverage
• Shotgun metagenomics provides an unbiased view of the entire microbial community
  • Fragmented DNA is sequenced without prior amplification
  • Allows for the discovery of novel genes and pathways
• Sequencing depth and coverage impact the ability to detect rare taxa and functions
• Paired-end sequencing improves assembly and resolves repetitive regions
• Multiplexing allows multiple samples to be sequenced simultaneously using unique barcodes
• Quality control metrics (Q scores, read length, GC content) assess sequencing performance

Bioinformatics Tools and Pipelines

• Quality filtering removes low-quality reads and trims adapters to improve downstream analysis
  • Tools: Trimmomatic, FastQC, PRINSEQ
• Sequence assembly reconstructs genomes and metagenomes from short reads
  • De novo assembly (MEGAHIT, SPAdes) does not require a reference genome
  • Reference-based assembly (Bowtie2, BWA) maps reads to known genomes
• Chimera detection identifies and removes artificially combined sequences
  • Tools: UCHIME, VSEARCH
• Sequence clustering groups similar reads into OTUs or ASVs
  • Tools: QIIME, Mothur, DADA2
• Taxonomic assignment matches sequences to reference databases (SILVA, Greengenes, RDP)
  • Naive Bayes classifiers (RDP Classifier) assign taxonomy based on k-mer composition
  • Sequence alignment tools (BLAST) identify closest matches in databases
• Gene prediction and annotation identify functional potential of microbiomes
  • Tools: Prodigal, MetaGeneMark, KEGG, COG

Data Analysis and Visualization

• Rarefaction curves assess sequencing depth and species richness
• Alpha diversity metrics (Chao1, Shannon, Simpson) quantify within-sample diversity
  • Plotted using box plots or bar charts to compare groups
• Beta diversity metrics (Bray-Curtis, UniFrac) measure between-sample differences
  • Visualized using Principal Coordinate Analysis (PCoA) or Non-Metric Multidimensional Scaling (NMDS)
• Heatmaps display relative abundances of taxa across samples
• Stacked bar plots show taxonomic composition at different levels (phylum, genus, species)
• Correlation analyses (Spearman, Pearson) identify associations between microbial taxa and metadata
• Statistical tests (ANOVA, PERMANOVA) assess significant differences between groups
• Machine learning methods (Random Forests, Support Vector Machines) predict sample categories based on microbiome profiles

Taxonomic Classification Methods

• Sequence similarity-based methods compare query sequences to reference databases
  • Best BLAST hit assigns taxonomy based on the top alignment score
  • Lowest common ancestor (LCA) algorithm (MEGAN) assigns shared taxonomy among multiple hits
• Composition-based methods use k-mer frequencies and machine learning to classify sequences
  • Naive Bayes classifier (RDP Classifier) calculates posterior probabilities for each taxonomic rank
  • k-Nearest Neighbors (k-NN) assigns taxonomy based on the majority vote of the k most similar sequences
• Phylogenetic placement methods insert query sequences into reference phylogenetic trees
  • Evolutionary placement algorithm (EPA) in RAxML places sequences based on maximum likelihood
  • pplacer uses Bayesian posterior probability to place sequences on a reference tree
• Marker gene-based methods rely on single-copy, evolutionarily conserved genes
  • MetaPhlAn2 uses clade-specific marker genes to estimate relative abundances
  • mOTU uses marker genes to profile taxonomic composition and functional potential

Functional Analysis of Microbiomes

• Gene prediction identifies open reading frames (ORFs) in assembled contigs
  • Tools: Prodigal, MetaGeneMark, FragGeneScan
• Functional annotation assigns predicted genes to functional categories
  • Databases: KEGG, COG, eggNOG, Pfam
  • Tools: BLAST, DIAMOND, InterProScan
• Pathway analysis maps annotated genes to metabolic pathways
  • Tools: KEGG Mapper, MetaCyc, HUMAnN2
• Comparative analysis identifies differentially abundant functions between groups
  • Tools: LEfSe, DESeq2, edgeR
• Metatranscriptomics assesses active gene expression in microbiomes
  • RNA sequencing (RNA-seq) quantifies transcript abundances
  • Differential expression analysis identifies genes responding to environmental changes
• Metaproteomics characterizes the functional activity of microbiomes at the protein level
  • Mass spectrometry identifies and quantifies proteins
  • Protein-protein interaction networks reveal functional associations

Applications and Case Studies

• Human gut microbiome studies link dysbiosis to diseases (obesity, inflammatory bowel disease, diabetes)
  • Fecal microbiota transplantation (FMT) is used to treat recurrent Clostridium difficile infection
  • Probiotics and prebiotics modulate the gut microbiome for therapeutic purposes
• Environmental microbiome studies assess biodiversity and monitor ecosystem health
  • Soil microbiomes influence plant growth and nutrient cycling
  • Marine microbiomes play crucial roles in global biogeochemical cycles
• Bioremediation uses microbial communities to degrade pollutants and clean up contaminated sites
  • Metagenomics identifies key microbial taxa and genes involved in bioremediation processes
  • Monitoring microbiome shifts helps optimize bioremediation strategies
• Agriculture applies microbiome knowledge to improve crop yields and disease resistance
  • Plant growth-promoting rhizobacteria (PGPR) enhance nutrient uptake and stress tolerance
  • Microbiome-based biocontrol agents suppress plant pathogens
• Personalized medicine leverages microbiome data for precision treatments
  • Microbiome-based biomarkers predict disease risk and treatment response
  • Targeted modulation of the microbiome through diet, probiotics, and fecal transplants