| Package Name | Access | Summary | Updated |
|---|---|---|---|
| bioconductor-splinter | public | Splice Interpreter of Transcripts | 2025-04-22 |
| bioconductor-suprahex | public | supraHex: a supra-hexagonal map for analysing tabular omics data | 2025-04-22 |
| bioconductor-spikeli | public | Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool | 2025-04-22 |
| bioconductor-spktools | public | Methods for Spike-in Arrays | 2025-04-22 |
| bioconductor-varianttools | public | Tools for Exploratory Analysis of Variant Calls | 2025-04-22 |
| bioconductor-splicegear | public | splicegear | 2025-04-22 |
| bioconductor-starr | public | Simple tiling array analysis of Affymetrix ChIP-chip data | 2025-04-22 |
| bioconductor-trio | public | Testing of SNPs and SNP Interactions in Case-Parent Trio Studies | 2025-04-22 |
| bioconductor-tweedeseq | public | RNA-seq data analysis using the Poisson-Tweedie family of distributions | 2025-04-22 |
| bioconductor-rlmm | public | A Genotype Calling Algorithm for Affymetrix SNP Arrays | 2025-04-22 |
| bioconductor-slqpcr | public | Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH | 2025-04-22 |
| bioconductor-sizepower | public | Sample Size and Power Calculation in Micorarray Studies | 2025-04-22 |
| bioconductor-sispa | public | SISPA: Method for Sample Integrated Set Profile Analysis | 2025-04-22 |
| bioconductor-similarpeak | public | Metrics to estimate a level of similarity between two ChIP-Seq profiles | 2025-04-22 |
| bioconductor-simbindprofiles | public | Similar Binding Profiles | 2025-04-22 |
| bioconductor-sigpathway | public | Pathway Analysis | 2025-04-22 |
| bioconductor-sights | public | Statistics and dIagnostic Graphs for HTS | 2025-04-22 |
| bioconductor-sgseq | public | Splice event prediction and quantification from RNA-seq data | 2025-04-22 |
| bioconductor-seqgsea | public | Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing | 2025-04-22 |
| bioconductor-sepa | public | Given single-cell RNA-seq data and true experiment time of cells or pseudo-time cell ordering, SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns. | 2025-04-22 |
| bioconductor-semdist | public | Information Accretion-based Function Predictor Evaluation | 2025-04-22 |
| bioconductor-segmentseq | public | Methods for identifying small RNA loci from high-throughput sequencing data | 2025-04-22 |
| bioconductor-scater | public | Single-Cell Analysis Toolkit for Gene Expression Data in R | 2025-04-22 |
| bioconductor-savr | public | Parse and analyze Illumina SAV files | 2025-04-22 |
| bioconductor-santa | public | Spatial Analysis of Network Associations | 2025-04-22 |
