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Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

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If you'd like to join our channel search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


r-genomes 3.42.0
Propagated dependencies: r-readr@2.2.0 r-curl@7.0.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/genomes
Licenses: GPL 3
Build system: r
Synopsis: Genome sequencing project metadata
Description:

Download genome and assembly reports from NCBI.

r-gcspikelite 1.50.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/gcspikelite
Licenses: LGPL 2.0+
Build system: r
Synopsis: Spike-in data for GC/MS data and methods within flagme
Description:

Spike-in data for GC/MS data and methods within flagme.

r-ggtreespace 1.8.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.2 r-tibble@3.3.1 r-rlang@1.1.7 r-phytools@2.5-2 r-interp@1.1-6 r-ggtree@4.0.4 r-ggplot2@4.0.2 r-ggally@2.4.0 r-dplyr@1.2.0 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/YuLab-SMU/ggtreeSpace
Licenses: Artistic License 2.0
Build system: r
Synopsis: Visualizing Phylomorphospaces using 'ggtree'
Description:

This package is a comprehensive visualization tool specifically designed for exploring phylomorphospace. It not only simplifies the process of generating phylomorphospace, but also enhances it with the capability to add graphic layers to the plot with grammar of graphics to create fully annotated phylomorphospaces. It also provide some utilities to help interpret evolutionary patterns.

r-gsreg 1.46.0
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-homo-sapiens@1.3.1 r-genomicfeatures@1.62.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GSReg
Licenses: GPL 2
Build system: r
Synopsis: Gene Set Regulation (GS-Reg)
Description:

This package provides a package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA).

r-genetonic 3.6.0
Propagated dependencies: r-visnetwork@2.1.4 r-viridis@0.6.5 r-tippy@0.1.0 r-tidyr@1.3.2 r-summarizedexperiment@1.40.0 r-shinywidgets@0.9.1 r-shinycssloaders@1.1.0 r-shinyace@0.4.4 r-shiny@1.11.1 r-scales@1.4.0 r-s4vectors@0.48.0 r-rmarkdown@2.30 r-rlang@1.1.7 r-rintrojs@0.3.4 r-rcolorbrewer@1.1-3 r-plotly@4.12.0 r-mosdef@1.6.0 r-matrixstats@1.5.0 r-igraph@2.2.2 r-go-db@3.22.0 r-ggridges@0.5.7 r-ggrepel@0.9.7 r-ggplot2@4.0.2 r-ggforce@0.5.0 r-expm@1.0-0 r-dynamictreecut@1.63-1 r-dt@0.34.0 r-dplyr@1.2.0 r-deseq2@1.50.2 r-dendextend@1.19.1 r-complexupset@1.3.3 r-complexheatmap@2.26.1 r-colourpicker@1.3.0 r-colorspace@2.1-2 r-circlize@0.4.17 r-bs4dash@2.3.5 r-backbone@3.0.4 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/federicomarini/GeneTonic
Licenses: Expat
Build system: r
Synopsis: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis
Description:

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

r-gseamining 1.22.0
Propagated dependencies: r-tidytext@0.4.3 r-tibble@3.3.1 r-stringr@1.6.0 r-rlang@1.1.7 r-gridextra@2.3 r-ggwordcloud@0.6.2 r-ggplot2@4.0.2 r-dplyr@1.2.0 r-dendextend@1.19.1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GSEAmining
Licenses: GPL 3 FSDG-compatible
Build system: r
Synopsis: Make Biological Sense of Gene Set Enrichment Analysis Outputs
Description:

Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study.

r-gwena 1.22.0
Propagated dependencies: r-wgcna@1.74 r-tidyr@1.3.2 r-tibble@3.3.1 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-rlist@0.4.6.2 r-rcolorbrewer@1.1-3 r-purrr@1.2.1 r-netrep@1.2.9 r-matrixstats@1.5.0 r-magrittr@2.0.4 r-igraph@2.2.2 r-gprofiler2@0.2.4 r-ggplot2@4.0.2 r-dynamictreecut@1.63-1 r-dplyr@1.2.0 r-cluster@2.1.8.2
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GWENA
Licenses: GPL 3
Build system: r
Synopsis: Pipeline for augmented co-expression analysis
Description:

The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions.

r-gwas-bayes 1.22.0
Propagated dependencies: r-memoise@2.0.1 r-matrix@1.7-4 r-mass@7.3-65 r-limma@3.66.0 r-ga@3.2.5 r-caret@7.0-1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GWAS.BAYES
Licenses: FSDG-compatible
Build system: r
Synopsis: Bayesian analysis of Gaussian GWAS data
Description:

This package is built to perform GWAS analysis using Bayesian techniques. Currently, GWAS.BAYES has functionality for the implementation of BICOSS (Williams, J., Ferreira, M. A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics), BGWAS (Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies." BMC Bioinformatics), and GINA. All methods currently are for the analysis of Gaussian phenotypes The research related to this package was supported in part by National Science Foundation awards DMS 1853549, DMS 1853556, and DMS 2054173.

r-genemeta 1.84.0
Propagated dependencies: r-genefilter@1.92.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GeneMeta
Licenses: Artistic License 2.0
Build system: r
Synopsis: MetaAnalysis for High Throughput Experiments
Description:

This package provides a collection of meta-analysis tools for analysing high throughput experimental data.

r-gars 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-mlseq@2.30.0 r-ggplot2@4.0.2 r-damirseq@2.24.0 r-cluster@2.1.8.2
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GARS
Licenses: GPL 2+
Build system: r
Synopsis: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets
Description:

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

r-gpls 1.84.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/gpls
Licenses: Artistic License 2.0
Build system: r
Synopsis: Classification using generalized partial least squares
Description:

Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification.

r-gewist 1.56.0
Propagated dependencies: r-car@3.1-5
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GEWIST
Licenses: GPL 2
Build system: r
Synopsis: Gene Environment Wide Interaction Search Threshold
Description:

This GEWIST package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions.

r-gdrcore 1.10.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-s4vectors@0.48.0 r-purrr@1.2.1 r-multiassayexperiment@1.36.1 r-gdrutils@1.10.0 r-futile-logger@1.4.9 r-data-table@1.18.2.1 r-checkmate@2.3.4 r-bumpymatrix@1.18.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/gdrplatform/gDRcore
Licenses: Artistic License 2.0
Build system: r
Synopsis: Processing functions and interface to process and analyze drug dose-response data
Description:

This package contains core functions to process and analyze drug response data. The package provides tools for normalizing, averaging, and calculation of gDR metrics data. All core functions are wrapped into the pipeline function allowing analyzing the data in a straightforward way.

r-gep2pep 1.31.0
Propagated dependencies: r-xml@3.99-0.22 r-rhdf5@2.54.1 r-iterators@1.0.14 r-gseabase@1.72.0 r-foreach@1.5.2 r-digest@0.6.39 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/gep2pep
Licenses: GPL 3
Build system: r
Synopsis: Creation and Analysis of Pathway Expression Profiles (PEPs)
Description:

Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively.

r-gse13015 1.20.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-preprocesscore@1.72.0 r-geoquery@2.78.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GSE13015
Licenses: FSDG-compatible
Build system: r
Synopsis: GEO accession data GSE13015_GPL6106 as a SummarizedExperiment
Description:

Microarray expression matrix platform GPL6106 and clinical data for 67 septicemic patients and made them available as GEO accession [GSE13015](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13015). GSE13015 data have been parsed into a SummarizedExperiment object available in ExperimentHub. This data data could be used as an example supporting BloodGen3Module R package.

r-graphalignment 1.76.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://www.thp.uni-koeln.de/~berg/GraphAlignment/
Licenses: FSDG-compatible
Build system: r
Synopsis: GraphAlignment
Description:

Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)).

r-gseabenchmarker 1.32.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-keggdzpathwaysgeo@1.50.0 r-keggandmetacoredzpathwaysgeo@1.32.0 r-experimenthub@3.0.0 r-enrichmentbrowser@2.42.0 r-edger@4.8.2 r-biocparallel@1.44.0 r-biocfilecache@3.0.0 r-biobase@2.70.0 r-annotationhub@4.0.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/waldronlab/GSEABenchmarkeR
Licenses: Artistic License 2.0
Build system: r
Synopsis: Reproducible GSEA Benchmarking
Description:

The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated.

r-ggpa 1.24.0
Propagated dependencies: r-sna@2.8 r-scales@1.4.0 r-rcpparmadillo@15.2.3-1 r-rcpp@1.1.1 r-network@1.20.0 r-matrixstats@1.5.0 r-ggally@2.4.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/dongjunchung/GGPA/
Licenses: GPL 2+
Build system: r
Synopsis: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture
Description:

Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. GGPA package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph.

r-gofan 1.0.0
Propagated dependencies: r-vctrs@0.7.1 r-scales@1.4.0 r-rlang@1.1.7 r-plotly@4.12.0 r-igraph@2.2.2 r-go-db@3.22.0 r-ggplot2@4.0.2 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/jianhong/GOfan
Licenses: GPL 3
Build system: r
Synopsis: Sunburst Plot for Enriched Gene Ontology Terms
Description:

GOfan provides an intuitive and compact visualization of Gene Ontology (GO) enrichment results using a sunburst layout inspired by SynGO, preserving hierarchical relationships among GO terms and allowing color-based encoding of information such as p-values or gene counts. By converting complex GO DAGs into clean, circular representations, it allows researchers to quickly grasp the hierarchical structure and biological significance of enriched terms. The interactive and customizable visualizations facilitate exploration of key GO categories, enhancing interpretation and presentation of enrichment analyses.

r-gpa 1.24.0
Propagated dependencies: r-vegan@2.7-2 r-shinybs@0.63.0 r-shiny@1.11.1 r-rcpp@1.1.1 r-plyr@1.8.9 r-ggrepel@0.9.7 r-ggplot2@4.0.2 r-dt@0.34.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://dongjunchung.github.io/GPA/
Licenses: GPL 2+
Build system: r
Synopsis: GPA (Genetic analysis incorporating Pleiotropy and Annotation)
Description:

This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture.

r-gopro 1.38.0
Propagated dependencies: r-s4vectors@0.48.0 r-rcpp@1.1.1 r-org-hs-eg-db@3.22.0 r-multiassayexperiment@1.36.1 r-iranges@2.44.0 r-go-db@3.22.0 r-foreach@1.5.2 r-doparallel@1.0.17 r-dendextend@1.19.1 r-bh@1.90.0-1 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/mi2-warsaw/GOpro
Licenses: GPL 3
Build system: r
Synopsis: Find the most characteristic gene ontology terms for groups of human genes
Description:

Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).

r-gaschyhs 1.50.0
Propagated dependencies: r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://genome-www.stanford.edu/yeast_stress/data/rawdata/complete_dataset.txt
Licenses: Artistic License 2.0
Build system: r
Synopsis: ExpressionSet for response of yeast to heat shock and other environmental stresses
Description:

Data from PMID 11102521.

r-geofastq 1.20.0
Propagated dependencies: r-xml2@1.5.2 r-stringr@1.6.0 r-rvest@1.0.5 r-rcurl@1.98-1.17 r-plyr@1.8.9 r-foreach@1.5.2 r-doparallel@1.0.17
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GEOfastq
Licenses: Expat
Build system: r
Synopsis: Downloads ENA Fastqs With GEO Accessions
Description:

GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA.

r-gemma-r 3.8.0
Propagated dependencies: r-tidyr@1.3.2 r-tibble@3.3.1 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-s4vectors@0.48.0 r-rlang@1.1.7 r-rappdirs@0.3.4 r-r-utils@2.13.0 r-memoise@2.0.1 r-magrittr@2.0.4 r-lubridate@1.9.5 r-kableextra@1.4.0 r-jsonlite@2.0.0 r-httr@1.4.8 r-glue@1.8.0 r-digest@0.6.39 r-data-table@1.18.2.1 r-bit64@4.6.0-1 r-biobase@2.70.0 r-base64enc@0.1-6 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://pavlidislab.github.io/gemma.R/
Licenses: FSDG-compatible
Build system: r
Synopsis: wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses
Description:

Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles.

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Total packages: 3017