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bioconductor-singlecelltk
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public |
Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data
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2025-03-25 |
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bioconductor-universalmotif
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public |
Import, Modify, and Export Motifs with R
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2025-03-25 |
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bioconductor-post
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public |
Projection onto Orthogonal Space Testing for High Dimensional Data
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2025-03-25 |
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bioconductor-platecore
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public |
Provides basic S4 data structures and routines for analyzing plate based flow cytometry data.
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2025-03-25 |
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bioconductor-cytoml
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public |
A GatingML Interface for Cross Platform Cytometry Data Sharing
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2025-03-25 |
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bioconductor-flowvs
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public |
Variance stabilization in flow cytometry (and microarrays)
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2025-03-25 |
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bioconductor-lymphoseq
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public |
Analyze high-throughput sequencing of T and B cell receptors
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2025-03-25 |
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bioconductor-opencyto
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public |
Hierarchical Gating Pipeline for flow cytometry data
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2025-03-25 |
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bioconductor-ibmq
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public |
integrated Bayesian Modeling of eQTL data
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2025-03-25 |
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bioconductor-frgepistasis
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public |
Epistasis Analysis for Quantitative Traits by Functional Regression Model
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2025-03-25 |
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bioconductor-rqt
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public |
rqt: utilities for gene-level meta-analysis
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2025-03-25 |
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bioconductor-flowstats
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public |
Statistical methods for the analysis of flow cytometry data
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2025-03-25 |
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bioconductor-proteoqc
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public |
An R package for proteomics data quality control
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2025-03-25 |
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bioconductor-pga
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public |
An package for identification of novel peptides by customized database derived from RNA-Seq
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2025-03-25 |
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bioconductor-shinytandem
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public |
Provides a GUI for rTANDEM
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2025-03-25 |
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bioconductor-prostar
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public |
Provides a GUI for DAPAR
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2025-03-25 |
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bioconductor-primirtss
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public |
Prediction of pri-miRNA Transcription Start Site
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2025-03-25 |
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bioconductor-vtpnet
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public |
variant-transcription factor-phenotype networks
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2025-03-25 |
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bioconductor-gwascat
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public |
representing and modeling data in the EMBL-EBI GWAS catalog
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2025-03-25 |
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bioconductor-gqtlstats
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public |
gQTLstats: computationally efficient analysis for eQTL and allied studies
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2025-03-25 |
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bioconductor-pi
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public |
Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level
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2025-03-25 |
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r-xgr
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public |
The central goal of XGR by Fang et al. (2016) <doi:10.1186/s13073-016-0384-y> is to provide a data interpretation system necessary to do "big data" science. It is designed to make a user-defined gene or SNP list (or genomic regions) more interpretable by comprehensively utilising ontology annotations and interaction networks to reveal relationships and enhance opportunities for biological discovery. XGR is unique in supporting a broad range of ontologies (including knowledge of biological and molecular functions, pathways, diseases and phenotypes - in both human and mouse) and different types of networks (including functional, physical and pathway interactions). There are two core functionalities of XGR. The first is to provide basic infrastructures for easy access to built-in ontologies and networks. The second is to support data interpretations via 1) enrichment analysis using either built-in or custom ontologies, 2) similarity analysis for calculating semantic similarity between genes (or SNPs) based on their ontology annotation profiles, 3) network analysis for identification of gene networks given a query list of (significant) genes, SNPs or genomic regions, and 4) annotation analysis for interpreting genomic regions using co-localised functional genomic annotations (such as open chromatin, epigenetic marks, TF binding sites and genomic segments) and using nearby gene annotations (by ontologies). Together with its web app, XGR aims to provide a user-friendly tool for exploring genomic relations at the gene, SNP and genomic region level.
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2025-03-25 |
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bioconductor-phantasus
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public |
Visual and interactive gene expression analysis
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2025-03-25 |
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bioconductor-r3cpet
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public |
3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process
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2025-03-25 |
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bioconductor-dapar
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public |
Tools for the Differential Analysis of Proteins Abundance with R
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2025-03-25 |
