The article describes the Bioconductor infrastructure for representing and computing on annotated genomic ranges, integrating genomic data with the statistical computing features of R. The core packages are IRanges, GenomicRanges, and GenomicFeatures, which provide scalable data structures for handling annotated ranges, including transcript structures, read alignments, and coverage vectors. These packages support efficient algorithms for overlap detection, nearest neighbor detection, and other range operations. The infrastructure supports over 80 other Bioconductor packages, facilitating sequence analysis, differential expression analysis, and visualization. The article also discusses the design and implementation of these packages, their integration with other R packages, and their application in various genomic analyses, such as ChIP-seq and RNA-seq data processing. Additionally, it highlights the importance of efficient data structures and metadata storage for genomic data, and provides examples of how these tools can be used to explore the genetics of protein-DNA binding and allele-specific binding events.The article describes the Bioconductor infrastructure for representing and computing on annotated genomic ranges, integrating genomic data with the statistical computing features of R. The core packages are IRanges, GenomicRanges, and GenomicFeatures, which provide scalable data structures for handling annotated ranges, including transcript structures, read alignments, and coverage vectors. These packages support efficient algorithms for overlap detection, nearest neighbor detection, and other range operations. The infrastructure supports over 80 other Bioconductor packages, facilitating sequence analysis, differential expression analysis, and visualization. The article also discusses the design and implementation of these packages, their integration with other R packages, and their application in various genomic analyses, such as ChIP-seq and RNA-seq data processing. Additionally, it highlights the importance of efficient data structures and metadata storage for genomic data, and provides examples of how these tools can be used to explore the genetics of protein-DNA binding and allele-specific binding events.