Top Bioconductor Packages

• DESeq2: Differential gene expression analysis based on the negative binomial distribution.
• edgeR: Differential expression analysis of RNA-Seq and other count data.
• limma: Linear models for microarray data analysis.
• GenomicRanges: Representation and manipulation of genomic intervals and variables.
• SummarizedExperiment: Container for storing high-throughput assay data and metadata.
• SingleCellExperiment: Container for single-cell RNA-Seq data.
• AnnotationHub: Access to a variety of genome annotation resources.
• BiocGenerics: S4 generic functions used across Bioconductor packages.
• Rsamtools: Input/output, manipulation, and analysis of SAM/BAM files.
• biomaRt: Interface to BioMart databases like Ensembl.
• tximport: Import and summarize transcript-level estimates for gene-level analysis.
• VariantAnnotation: Annotation of variants detected by high-throughput sequencing.
• GenomicFeatures: Representation and manipulation of transcript annotation databases.
• Biostrings: Efficient manipulation of biological strings.
• Gviz: Plotting data and annotation information along genomic coordinates.
• ComplexHeatmap: Making complex, annotated heatmaps.
• scran: Methods for single-cell RNA-Seq data analysis.
• scater: Single-cell analysis tools for quality control, normalization, and visualization.
• IRanges: Infrastructure for representing and manipulating intervals.
• HDF5Array: HDF5 backend for DelayedArray objects.
• TCGAbiolinks: Tools for downloading, preparing, and analyzing TCGA data.
• edgeRun: High-performance differential expression analysis for RNA-Seq data.
• ChIPseeker: Annotation of peaks in ChIP-Seq data.
• clusterProfiler: Statistical analysis and visualization of functional profiles for genes and gene clusters.
• ensembldb: Utilities for working with Ensembl-based annotations.
• GEOquery: Get data from NCBI Gene Expression Omnibus (GEO).
• pathview: Pathway-based data integration and visualization.
• GSEABase: Gene set enrichment data structures and methods.
• GOstats: Tools for manipulating GO and microarray data.
• GO.db: A set of annotation maps describing the entire Gene Ontology.
• WGCNA: Weighted correlation network analysis for gene expression data.
• maftools: Analysis and visualization of mutation annotation format (MAF) files.
• DiffBind: Differential binding analysis of ChIP-Seq peak data.
• BSgenome: Infrastructure for Biostrings-based genome data packages.
• Rhtslib: High-throughput sequencing library as used by Rsamtools.
• ShortRead: Import and analyze high-throughput sequencing data.
• BiocParallel: Bioconductor facilities for parallel evaluation.
• msigdb: Import Molecular Signatures Database (MSigDB) gene sets.
• goseq: Gene Ontology analysis for RNA-Seq data.
• ReactomePA: Pathway enrichment analysis with Reactome Pathway Database.
• IRanges: Infrastructure for manipulating intervals on sequences.
• sva: Surrogate variable analysis for removing batch effects and other unwanted variation in high-throughput experiments.
• AnnotationForge: Tools for building SQLite-based annotation data packages.
• BioCycData: Access to BioCyc Pathway/Genome Database Collection.
• biovizBase: Basic graphic utilities for visualization of genomic data.
• scRNAseq: Single-cell RNA sequencing data package.
• TxDb.Hsapiens.UCSC.hg38.knownGene: Annotation package for TxDb object(s).
• SummarizedExperiment: Container for storing high-throughput assay data and metadata.
• GenomicAlignments: Representation and manipulation of short genomic alignments.
• rtracklayer: Extensible framework for interacting with multiple genome browsers.

Top General R Packages

• ggplot2: Data visualization package based on the Grammar of Graphics.
• dplyr: Data manipulation functions that simplify complex operations on data frames.
• tidyr: Tools for converting data to tidy format.
• stringr: Simple, consistent functions to manipulate strings.
• lubridate: Functions to work with date-times and timespans.
• shiny: Web application framework for R.
• caret: Classification and regression training package.
• rmarkdown: Dynamic documents for R.
• knitr: A general-purpose literate programming engine.
• data.table: Extension of data.frame for fast manipulation of large datasets.
• xtable: Export tables to LaTeX or HTML.
• forecast: Tools for forecasting and time series analysis.
• randomForest: Classification and regression based on a forest of trees using random inputs.
• survival: Survival analysis, including penalized likelihood.
• glmnet: Lasso and elastic-net regularized generalized linear models.
• plotly: Interactive, web-based graphs via plotly’s JavaScript graphing library.
• sf: Simple features for R, for handling vector data.
• zoo: S3 infrastructure for regular and irregular time series.
• tm: Text mining package for text mining applications.
• lme4: Linear mixed-effects models using ‘Eigen’ and S4.
• httr: Tools for working with URLs and HTTP.
• rcpp: Seamless R and C++ integration.
• sp: Classes and methods for spatial data.
• leaflet: Create interactive web maps with the JavaScript ‘Leaflet’ library.
• MASS: Functions and datasets to support Venables and Ripley’s MASS book.
• readr: Read rectangular data (csv, tsv, fwf).
• magrittr: Provides a mechanism for chaining commands with a new forward-pipe operator.
• haven: Import and export ‘SPSS’, ‘Stata’ and ‘SAS’ files.
• tibble: Modern re-imagining of data frames.
• purrr: Functional programming tools.
• janitor: Simple tools for examining and cleaning dirty data.
• forcats: Tools for working with categorical variables (factors).
• sparklyr: R interface for Apache Spark.
• odbc: Connect to ODBC compatible databases.
• curl: A Modern and Flexible Web Client for R.
• jsonlite: A Simple and Robust JSON Parser and Generator for R.
• xml2: A modern XML package.
• RCurl: General network (HTTP/FTP/…) client interface for R.
• highcharter: A wrapper for the ‘Highcharts’ library.
• DT: A wrapper of the DataTables JavaScript library.
• shiny: Easy interactive web applications with R.
• flexdashboard: R Markdown Format for Flexible Dashboards.
• DiagrammeR: Create graph diagrams and flowcharts using R.
• visNetwork: Network visualization using vis.js library.
• sf: Simple features for R.
• tmap: Thematic maps.
• mapview: Interactive viewing of spatial data.
• plotly: Create interactive web graphics via ‘plotly.js’.
• dygraphs: Interface to ‘dygraphs’ JavaScript Charting Library.
• threejs: Interactive 3D scatter plots and globes.

This extended list provides a comprehensive overview of the most widely used Bioconductor and general R packages essential for bioinformatics, data analysis, and visualization. For more detailed information and exploration of these packages, you can visit the Bioconductor website and CRAN.