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Package Name Access Summary Updated
bioconductor-pga public An package for identification of novel peptides by customized database derived from RNA-Seq 2025-04-22
bioconductor-shinytandem public Provides a GUI for rTANDEM 2025-04-22
bioconductor-prostar public Provides a GUI for DAPAR 2025-04-22
bioconductor-primirtss public Prediction of pri-miRNA Transcription Start Site 2025-04-22
bioconductor-vtpnet public variant-transcription factor-phenotype networks 2025-04-22
bioconductor-gwascat public representing and modeling data in the EMBL-EBI GWAS catalog 2025-04-22
bioconductor-gqtlstats public gQTLstats: computationally efficient analysis for eQTL and allied studies 2025-04-22
bioconductor-pi public Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level 2025-04-22
r-xgr 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. 2025-04-22
bioconductor-phantasus public Visual and interactive gene expression analysis 2025-04-22
bioconductor-r3cpet public 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process 2025-04-22
bioconductor-dapar public Tools for the Differential Analysis of Proteins Abundance with R 2025-04-22
bioconductor-flowfit public Estimate proliferation in cell-tracking dye studies 2025-04-22
bioconductor-cytodx public Robust prediction of clinical outcomes using cytometry data without cell gating 2025-04-22
bioconductor-chrogps public chroGPS2: Generation, visualization and differential analysis of epigenome maps 2025-04-22
bioconductor-chic public Quality Control Pipeline for ChIP-Seq Data 2025-04-22
bioconductor-rforproteomics public Companion package to the 'Using R and Bioconductor for proteomics data analysis' publication 2025-04-22
bioconductor-msmstests public LC-MS/MS Differential Expression Tests 2025-04-22
bioconductor-dapardata public Data accompanying the DAPAR and Prostar packages 2025-04-22
bioconductor-categorycompare public Meta-analysis of high-throughput experiments using feature annotations 2025-04-22
bioconductor-immunespacer public A Thin Wrapper around the ImmuneSpace Data and Tools Portal 2025-04-22
bioconductor-bioseqclass public Classification for Biological Sequences 2025-04-22
bioconductor-tfarm public Transcription Factors Association Rules Miner 2025-04-22
bioconductor-openprimerui public Shiny Application for Multiplex PCR Primer Design and Analysis 2025-04-22
bioconductor-openprimer public Multiplex PCR Primer Design and Analysis 2025-04-22

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