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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.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

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


r-gsgalgor 1.20.0
Propagated dependencies: r-survival@3.8-3 r-proxy@0.4-27 r-nsga2r@1.1 r-matchingr@2.0.0 r-foreach@1.5.2 r-doparallel@1.0.17 r-cluster@2.1.8.1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/harpomaxx/GSgalgoR
Licenses: Expat
Build system: r
Synopsis: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer
Description:

This package provides a multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The Galgo framework combines the advantages of clustering algorithms for grouping heterogeneous omics data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

r-genefu 2.42.0
Propagated dependencies: r-survcomp@1.60.0 r-mclust@6.1.2 r-limma@3.66.0 r-impute@1.84.0 r-ic10trainingdata@2.0.1 r-ic10@2.0.2 r-biomart@2.66.0 r-amap@0.8-20 r-aims@1.42.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://www.pmgenomics.ca/bhklab/software/genefu
Licenses: Artistic License 2.0
Build system: r
Synopsis: Computation of Gene Expression-Based Signatures in Breast Cancer
Description:

This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis.

r-gghumanmethcancerpanelv1-db 1.4.1
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-annotationforge@1.52.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GGHumanMethCancerPanelv1.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Illumina Golden Gate Human Methylation Cancer Panel Version 1 annotation data (chip GGHumanMethCancerPanelv1)
Description:

Illumina Golden Gate Human Methylation Cancer Panel Version 1 annotation data (chip GGHumanMethCancerPanelv1) assembled using data from public repositories.

r-guideseq 1.40.0
Propagated dependencies: r-tidyr@1.3.1 r-stringr@1.6.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-rlang@1.1.6 r-rio@1.2.4 r-pwalign@1.6.0 r-purrr@1.2.0 r-patchwork@1.3.2 r-openxlsx@4.2.8.1 r-multtest@2.66.0 r-matrixstats@1.5.0 r-limma@3.66.0 r-iranges@2.44.0 r-hash@2.2.6.3 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-crisprseek@1.50.0 r-chippeakanno@3.44.0 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GUIDEseq
Licenses: GPL 2+
Build system: r
Synopsis: GUIDE-seq and PEtag-seq analysis pipeline
Description:

The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels.

r-gdcrnatools 1.30.0
Propagated dependencies: r-xml@3.99-0.20 r-survminer@0.5.1 r-survival@3.8-3 r-shiny@1.11.1 r-rjson@0.2.23 r-pathview@1.50.0 r-org-hs-eg-db@3.22.0 r-limma@3.66.0 r-jsonlite@2.0.0 r-gplots@3.2.0 r-ggplot2@4.0.1 r-genomicdatacommons@1.34.1 r-edger@4.8.0 r-dt@0.34.0 r-dose@4.4.0 r-deseq2@1.50.2 r-clusterprofiler@4.18.2 r-biomart@2.66.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GDCRNATools
Licenses: Artistic License 2.0
Build system: r
Synopsis: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
Description:

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

r-genarise 1.86.0
Propagated dependencies: r-xtable@1.8-4 r-tkrplot@0.0-30 r-locfit@1.5-9.12
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://www.ifc.unam.mx/genarise
Licenses: FSDG-compatible
Build system: r
Synopsis: Microarray Analysis tool
Description:

genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer.

r-genomicplot 1.8.1
Propagated dependencies: r-viridis@0.6.5 r-venndiagram@1.7.3 r-txdbmaker@1.6.0 r-tidyr@1.3.1 r-seqinfo@1.0.0 r-scales@1.4.0 r-rtracklayer@1.70.0 r-rsamtools@2.26.0 r-rcas@1.36.0 r-plyranges@1.30.1 r-iranges@2.44.0 r-ggsignif@0.6.4 r-ggsci@4.1.0 r-ggpubr@0.6.2 r-ggplotify@0.1.3 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-genomation@1.42.0 r-edger@4.8.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-complexheatmap@2.26.0 r-circlize@0.4.16 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/shuye2009/GenomicPlot
Licenses: GPL 2
Build system: r
Synopsis: Plot profiles of next generation sequencing data in genomic features
Description:

Visualization of next generation sequencing (NGS) data is essential for interpreting high-throughput genomics experiment results. GenomicPlot facilitates plotting of NGS data in various formats (bam, bed, wig and bigwig); both coverage and enrichment over input can be computed and displayed with respect to genomic features (such as UTR, CDS, enhancer), and user defined genomic loci or regions. Statistical tests on signal intensity within user defined regions of interest can be performed and represented as boxplots or bar graphs. Parallel processing is used to speed up computation on multicore platforms. In addition to genomic plots which is suitable for displaying of coverage of genomic DNA (such as ChIPseq data), metagenomic (without introns) plots can also be made for RNAseq or CLIPseq data as well.

r-geometadb 1.72.0
Propagated dependencies: r-rsqlite@2.4.4 r-r-utils@2.13.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GEOmetadb
Licenses: Artistic License 2.0
Build system: r
Synopsis: compilation of metadata from NCBI GEO
Description:

The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 .

r-gmicr 1.24.0
Propagated dependencies: r-wgcna@1.73 r-shiny@1.11.1 r-reshape2@1.4.5 r-org-mm-eg-db@3.22.0 r-org-hs-eg-db@3.22.0 r-gseabase@1.72.0 r-grbase@2.0.3 r-grain@1.4.5 r-gostats@2.76.0 r-foreach@1.5.2 r-dt@0.34.0 r-doparallel@1.0.17 r-data-table@1.17.8 r-category@2.76.0 r-bnlearn@5.1 r-ape@5.8-1 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GmicR
Licenses: FSDG-compatible
Build system: r
Synopsis: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC)
Description:

This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool.

r-geneticsped 1.72.0
Propagated dependencies: r-mass@7.3-65 r-genetics@1.3.8.1.3 r-gdata@3.0.1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: http://rgenetics.org
Licenses: LGPL 2.1+ FSDG-compatible
Build system: r
Synopsis: Pedigree and genetic relationship functions
Description:

This package provides classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care!

r-genesis 2.40.0
Propagated dependencies: r-snprelate@1.44.0 r-seqvartools@1.48.0 r-seqarray@1.50.0 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-matrix@1.7-4 r-iranges@2.44.0 r-igraph@2.2.1 r-gwastools@1.56.0 r-genomicranges@1.62.0 r-gdsfmt@1.46.0 r-data-table@1.17.8 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/UW-GAC/GENESIS
Licenses: GPL 3
Build system: r
Synopsis: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness
Description:

The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.

r-genomeintervals 1.66.0
Propagated dependencies: r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-iranges@2.44.0 r-intervals@0.15.5 r-genomicranges@1.62.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/genomeIntervals
Licenses: Artistic License 2.0
Build system: r
Synopsis: Operations on genomic intervals
Description:

This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package girafe'.

r-globalseq 1.38.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/rauschenberger/globalSeq
Licenses: GPL 3
Build system: r
Synopsis: Global Test for Counts
Description:

The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.

r-geodiff 1.16.0
Propagated dependencies: r-withr@3.0.2 r-testthat@3.3.0 r-roptim@0.1.7 r-robust@0.7-5 r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-plyr@1.8.9 r-nanostringnctools@1.18.0 r-matrix@1.7-4 r-lme4@1.1-37 r-geomxtools@3.14.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/Nanostring-Biostats/GeoDiff
Licenses: Expat
Build system: r
Synopsis: Count model based differential expression and normalization on GeoMx RNA data
Description:

This package provides a series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette.

r-graper 1.26.0
Propagated dependencies: r-rcpparmadillo@15.2.2-1 r-rcpp@1.1.0 r-matrixstats@1.5.0 r-matrix@1.7-4 r-ggplot2@4.0.1 r-cowplot@1.2.0 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/graper
Licenses: GPL 2+
Build system: r
Synopsis: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes
Description:

This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach.

r-gdrcore 1.8.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-s4vectors@0.48.0 r-purrr@1.2.0 r-multiassayexperiment@1.36.1 r-gdrutils@1.8.0 r-futile-logger@1.4.3 r-data-table@1.17.8 r-checkmate@2.3.3 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-gpa 1.22.0
Propagated dependencies: r-vegan@2.7-2 r-shinybs@0.61.1 r-shiny@1.11.1 r-rcpp@1.1.0 r-plyr@1.8.9 r-ggrepel@0.9.6 r-ggplot2@4.0.1 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-gewist 1.54.0
Propagated dependencies: r-car@3.1-3
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-ggkegg 1.8.0
Propagated dependencies: r-xml@3.99-0.20 r-tidygraph@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-shadowtext@0.1.6 r-patchwork@1.3.2 r-magick@2.9.0 r-igraph@2.2.1 r-gtable@0.3.6 r-ggraph@2.2.2 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-data-table@1.17.8 r-biocfilecache@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://github.com/noriakis/ggkegg
Licenses: Expat
Build system: r
Synopsis: Analyzing and visualizing KEGG information using the grammar of graphics
Description:

This package aims to import, parse, and analyze KEGG data such as KEGG PATHWAY and KEGG MODULE. The package supports visualizing KEGG information using ggplot2 and ggraph through using the grammar of graphics. The package enables the direct visualization of the results from various omics analysis packages.

r-gsealm 1.70.0
Propagated dependencies: r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GSEAlm
Licenses: Artistic License 2.0
Build system: r
Synopsis: Linear Model Toolset for Gene Set Enrichment Analysis
Description:

Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics.

r-gaga 2.56.0
Propagated dependencies: r-mgcv@1.9-4 r-ebarrays@2.74.0 r-coda@0.19-4.1 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/gaga
Licenses: GPL 2+
Build system: r
Synopsis: GaGa hierarchical model for high-throughput data analysis
Description:

This package implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

r-gloscope 2.0.1
Propagated dependencies: r-vegan@2.7-2 r-singlecellexperiment@1.32.0 r-rlang@1.1.6 r-rcolorbrewer@1.1-3 r-rann@2.6.2 r-pheatmap@1.0.13 r-permute@0.9-8 r-mvnfast@0.2.8 r-mclust@6.1.2 r-mass@7.3-65 r-ggplot2@4.0.1 r-fnn@1.1.4.1 r-cluster@2.1.8.1 r-boot@1.3-32 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GloScope
Licenses: Artistic License 2.0
Build system: r
Synopsis: Population-level Representation on scRNA-Seq data
Description:

This package aims at representing and summarizing the entire single-cell profile of a sample. It allows researchers to perform important bioinformatic analyses at the sample-level such as visualization and quality control. The main functions Estimate sample distribution and calculate statistical divergence among samples, and visualize the distance matrix through MDS plots.

r-genomictuples 1.44.0
Propagated dependencies: r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rcpp@1.1.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-data-table@1.17.8 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: www.github.com/PeteHaitch/GenomicTuples
Licenses: Artistic License 2.0
Build system: r
Synopsis: Representation and Manipulation of Genomic Tuples
Description:

GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package.

r-genproseq 1.14.0
Propagated dependencies: r-word2vec@0.4.1 r-ttgsea@1.18.0 r-tensorflow@2.20.0 r-reticulate@1.44.1 r-mclust@6.1.2 r-keras@2.16.0 r-deeppincs@1.18.0 r-catencoders@0.1.1
Channel: guix-bioc
Location: guix-bioc/packages/g.scm (guix-bioc packages g)
Home page: https://bioconductor.org/packages/GenProSeq
Licenses: Artistic License 2.0
Build system: r
Synopsis: Generating Protein Sequences with Deep Generative Models
Description:

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.

Total results: 2909