Category: Lab Notes: Genomics

Metagenomics provides a culture-independent approach for studying antimicrobial resistance across microbial communities. Learn how resistance genes are detected and analyzed, how resistomes are characterized, and how metagenomic sequencing can investigate resistance in human, animal, agricultural, food, wastewater, and environmental systems.

Metagenomic statistical analysis helps researchers compare microbial communities, identify differentially abundant taxa and genes, analyze diversity, evaluate associations, and interpret functional and genomic patterns. Learn the key statistical methods, challenges, and best practices for reliable metagenomic analysis.

Metagenomic databases provide the reference information needed to interpret microbial DNA sequences. Explore database types, selection strategies, taxonomic and functional resources, specialized databases, applications, challenges, and best practices for reproducible metagenomic analysis.

Metagenomic functional annotation assigns potential biological functions to genes identified in microbial communities. Learn how functional annotation works, the role of databases and protein domains, metabolic pathway analysis, specialized gene annotation, applications, and limitations.

Metagenomic gene prediction identifies potential protein-coding genes and genomic features within metagenomic sequences and reconstructed microbial genomes. Learn how gene prediction works, how gene catalogs are created, major methods, challenges, and applications in functional metagenomics.

Metagenomic binning is a key step in genome-resolved metagenomics that groups assembled contigs into genome-like collections. Learn how binning works, the signals and algorithms used, genome quality assessment, MAG recovery, applications, and major challenges.

Metagenomic functional profiling reveals the genes, metabolic pathways, and biological capabilities encoded within microbial communities. Learn about gene prediction, functional annotation, pathway analysis, abundance estimation, antimicrobial resistance, genome-resolved analysis, applications, and limitations.

Metagenomic taxonomic profiling identifies microorganisms in sequencing data and estimates their abundance. Learn about taxonomic classification, reference databases, species identification, microbial diversity, contamination, and applications of metagenomic profiling.

Metagenomic quality control is essential for producing reliable sequencing data for downstream analysis. Learn how raw FASTQ reads are assessed, filtered, trimmed, and checked for contamination, host DNA, sequencing artifacts, and other quality issues before taxonomic, functional, and genomic analysis.

Metagenomic library preparation converts extracted microbial DNA into sequencing-ready libraries. Learn about DNA fragmentation, adapter ligation, indexing, amplification, library quantification, pooling, quality control, and preparation methods for short- and long-read sequencing.

Shotgun metagenomics sequences DNA from across microbial genomes to reveal community composition, genes, metabolic pathways, and functional potential. Explore its workflow, applications, advantages, and limitations.

Gene-tree/species-tree reconciliation compares gene trees with species trees to reconstruct evolutionary events such as gene duplication and gene loss. Learn the principles, workflow, applications, limitations, and role of reconciliation in comparative genomics and phylogenetics.

Gene duplication and gene loss are major forces in genome evolution. Discover how duplication creates new gene copies, how gene loss reshapes genomes, and how these processes influence gene families and functional diversification.

Gene family expansion and contraction describe changes in the number of related genes across evolutionary lineages. Learn how gene duplication, gene loss, functional diversification, phylogenetics, synteny, and comparative genomics help explain gene family evolution.

Gene family clustering groups related genes or proteins using sequence similarity, graph-based methods, hierarchical clustering, orthology, and phylogenetics. Learn the major approaches and their applications in comparative genomics.