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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-ribosomeprofilingqc 1.24.0
Propagated dependencies: r-xvector@0.50.0 r-txdbmaker@1.6.2 r-seqinfo@1.0.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-ruvseq@1.44.0 r-rtracklayer@1.70.1 r-rsubread@2.24.0 r-rsamtools@2.26.0 r-motifstack@1.54.0 r-iranges@2.44.0 r-ggrepel@0.9.7 r-ggplot2@4.0.2 r-ggfittext@0.10.3 r-ggextra@0.11.0 r-genomicranges@1.62.1 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.2 r-edaseq@2.44.0 r-cluster@2.1.8.2 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/ribosomeProfilingQC
Licenses: FSDG-compatible
Build system: r
Synopsis: Ribosome Profiling Quality Control
Description:

Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.

r-rcaspar 1.58.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RCASPAR
Licenses: GPL 3+
Build system: r
Synopsis: package for survival time prediction based on a piecewise baseline hazard Cox regression model.
Description:

The package is the R-version of the C-based software \boldCASPAR (Kaderali,2006: \urlhttp://bioinformatics.oxfordjournals.org/content/22/12/1495). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine.

r-rtn 2.36.0
Propagated dependencies: r-viper@1.46.0 r-summarizedexperiment@1.40.0 r-snow@0.4-4 r-s4vectors@0.48.0 r-reder@3.8.0 r-pwr@1.3-0 r-pheatmap@1.0.13 r-mixtools@2.0.0.1 r-minet@3.68.0 r-limma@3.66.0 r-iranges@2.44.0 r-igraph@2.2.2 r-data-table@1.18.2.1 r-car@3.1-5
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: http://dx.doi.org/10.1038/ncomms3464
Licenses: Artistic License 2.0
Build system: r
Synopsis: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons
Description:

This package provides a transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.

r-rnamodr-ml 1.26.0
Propagated dependencies: r-s4vectors@0.48.0 r-rnamodr@1.26.0 r-ranger@0.18.0 r-iranges@2.44.0 r-genomicranges@1.62.1 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/FelixErnst/RNAmodR.ML
Licenses: Artistic License 2.0
Build system: r
Synopsis: Detecting patterns of post-transcriptional modifications using machine learning
Description:

RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged.

r-rnaseqcovarimpute 1.10.0
Propagated dependencies: r-rlang@1.1.7 r-mice@3.19.0 r-magrittr@2.0.4 r-limma@3.66.0 r-foreach@1.5.2 r-edger@4.8.2 r-dplyr@1.2.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/brennanhilton/RNAseqCovarImpute
Licenses: GPL 3
Build system: r
Synopsis: Impute Covariate Data in RNA Sequencing Studies
Description:

The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR).

r-redisparam 1.14.0
Dependencies: hiredis@1.1.0
Propagated dependencies: r-withr@3.0.2 r-redux@1.1.5 r-logger@0.4.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RedisParam
Licenses: Artistic License 2.0
Build system: r
Synopsis: Provide a 'redis' back-end for BiocParallel
Description:

This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The manager distributes tasks to a worker pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative load balancing task distributions.

r-rrbsdata 1.32.0
Propagated dependencies: r-biseq@1.52.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RRBSdata
Licenses: LGPL 3
Build system: r
Synopsis: An RRBS data set with 12 samples and 10,000 simulated DMRs
Description:

RRBS data set comprising 12 samples with simulated differentially methylated regions (DMRs).

r-rseqan 1.32.0
Propagated dependencies: r-rcpp@1.1.1
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RSeqAn
Licenses: Modified BSD
Build system: r
Synopsis: R SeqAn
Description:

Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R.

r-rgsea 1.46.0
Propagated dependencies: r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RGSEA
Licenses: FSDG-compatible
Build system: r
Synopsis: Random Gene Set Enrichment Analysis
Description:

Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements.

r-rnaeditr 1.22.0
Propagated dependencies: r-survival@3.8-6 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-plyr@1.8.9 r-logistf@1.26.1 r-iranges@2.44.0 r-genomicranges@1.62.1 r-corrplot@0.95 r-bumphunter@1.52.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/TransBioInfoLab/rnaEditr
Licenses: GPL 3
Build system: r
Synopsis: Statistical analysis of RNA editing sites and hyper-editing regions
Description:

RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models.

r-rmassbank 3.22.0
Dependencies: openbabel@3.1.1
Propagated dependencies: r-yaml@2.3.12 r-xml@3.99-0.22 r-webchem@1.3.1 r-tidyselect@1.2.1 r-tibble@3.3.1 r-s4vectors@0.48.0 r-rjson@0.2.23 r-readr@2.2.0 r-readjdx@0.6.4 r-rcpp@1.1.1 r-rcdk@3.8.2 r-r-utils@2.13.0 r-purrr@1.2.1 r-mzr@2.44.0 r-msnbase@2.36.0 r-logger@0.4.1 r-httr2@1.2.2 r-httr@1.4.8 r-glue@1.8.0 r-envipat@2.8 r-dplyr@1.2.0 r-digest@0.6.39 r-data-table@1.18.2.1 r-chemminer@3.62.0 r-biobase@2.70.0 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RMassBank
Licenses: Artistic License 2.0
Build system: r
Synopsis: Workflow to process tandem MS files and build MassBank records
Description:

Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records.

r-rigvf 1.4.0
Propagated dependencies: r-whisker@0.4.1 r-tidyr@1.3.2 r-seqinfo@1.0.0 r-rlang@1.1.7 r-rjsoncons@1.3.3 r-memoise@2.0.1 r-jsonlite@2.0.0 r-iranges@2.44.0 r-httr2@1.2.2 r-genomicranges@1.62.1 r-dplyr@1.2.0 r-cachem@1.1.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://IGVF.github.io/rigvf
Licenses: Expat
Build system: r
Synopsis: R interface to the IGVF Catalog
Description:

The IGVF Catalog provides data on the impact of genomic variants on function. The `rigvf` package provides an interface to the IGVF Catalog, allowing easy integration with Bioconductor resources.

r-rtnsurvival 1.36.0
Propagated dependencies: r-survival@3.8-6 r-scales@1.4.0 r-rtnduals@1.36.0 r-rtn@2.36.0 r-rcolorbrewer@1.1-3 r-pheatmap@1.0.13 r-ggplot2@4.0.2 r-egg@0.4.5 r-dunn-test@1.3.7 r-data-table@1.18.2.1
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RTNsurvival
Licenses: Artistic License 2.0
Build system: r
Synopsis: Survival analysis using transcriptional networks inferred by the RTN package
Description:

RTNsurvival integrates regulons inferred by the RTN package with survival data. For each regulon, a two-tailed GSEA framework computes a differential Enrichment Score (dES) at the individual-sample level. The resulting dES distribution across samples is then used to evaluate survival associations within the cohort. Two primary workflows are supported: (i) Cox proportional hazards models, in which regulon activities are treated as predictors of survival time, and (ii) Kaplan–Meier analyses assessing cohort stratification based on regulon activity. All graphical outputs are customizable according to user specifications.

r-rtcga-clinical 20151101.42.0
Propagated dependencies: r-rtcga@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RTCGA.clinical
Licenses: GPL 2
Build system: r
Synopsis: Clinical datasets from The Cancer Genome Atlas Project
Description:

Package provides clinical datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Clinical data format is explained here https://wiki.nci.nih.gov/display/TCGA/Clinical+Data+Overview. Data from 2015-11-01 snapshot.

r-rtcga-mutations 20151101.42.0
Propagated dependencies: r-rtcga@1.40.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RTCGA.mutations
Licenses: GPL 2
Build system: r
Synopsis: Mutations datasets from The Cancer Genome Atlas Project
Description:

Package provides mutations datasets from The Cancer Genome Atlas Project for all cohorts types from http://gdac.broadinstitute.org/. Mutations data format is explained here https://wiki.nci.nih.gov/display/TCGA/Mutation+Annotation+Format+(MAF)+Specification. There is extra one column with patients barcodes. Data from 2015-11-01 snapshot.

r-rcx 1.16.0
Propagated dependencies: r-plyr@1.8.9 r-jsonlite@2.0.0 r-igraph@2.2.2
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/frankkramer-lab/RCX
Licenses: Expat
Build system: r
Synopsis: R package implementing the Cytoscape Exchange (CX) format
Description:

Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape.

r-regutools 1.24.0
Propagated dependencies: r-s4vectors@0.48.0 r-rsqlite@2.4.6 r-rcy3@2.30.1 r-iranges@2.44.0 r-gviz@1.54.0 r-genomicranges@1.62.1 r-dbi@1.3.0 r-biostrings@2.78.0 r-biocfilecache@3.0.0 r-annotationhub@4.0.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/ComunidadBioInfo/regutools
Licenses: Artistic License 2.0
Build system: r
Synopsis: regutools: an R package for data extraction from RegulonDB
Description:

RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.

r-ragene10stv1probe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/ragene10stv1probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type ragene10stv1
Description:

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was RaGene-1\_0-st-v1\_probe\_tab.

r-rat2302-db 3.13.0
Propagated dependencies: r-org-rn-eg-db@3.23.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/rat2302.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix Affymetrix Rat230_2 Array annotation data (chip rat2302)
Description:

Affymetrix Affymetrix Rat230_2 Array annotation data (chip rat2302) assembled using data from public repositories.

r-rnaseqsamplesize 2.22.0
Propagated dependencies: r-tidyselect@1.2.1 r-tidyr@1.3.2 r-summarizedexperiment@1.40.0 r-rnaseqsamplesizedata@1.44.0 r-recount@1.36.0 r-rcpp@1.1.1 r-matlab@1.0.4.1 r-keggrest@1.50.0 r-heatmap3@1.1.9 r-ggpubr@0.6.3 r-ggplot2@4.0.2 r-edger@4.8.2 r-dplyr@1.2.0 r-biomart@2.66.1
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RnaSeqSampleSize
Licenses: GPL 2+
Build system: r
Synopsis: RnaSeqSampleSize
Description:

RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

r-rcm 1.28.0
Propagated dependencies: r-vgam@1.1-14 r-tseries@0.10-60 r-tensor@1.5.1 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-phyloseq@1.54.1 r-nleqslv@3.3.5 r-mass@7.3-65 r-ggplot2@4.0.2 r-edger@4.8.2 r-alabama@2025.1.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/
Licenses: GPL 2
Build system: r
Synopsis: Fit row-column association models with the negative binomial distribution for the microbiome
Description:

Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available.

r-rpa 1.68.0
Propagated dependencies: r-rmarkdown@2.30 r-phyloseq@1.54.1 r-biocstyle@2.38.0 r-biocgenerics@0.56.0 r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/antagomir/RPA
Licenses: FreeBSD
Build system: r
Synopsis: RPA: Robust Probabilistic Averaging for probe-level analysis
Description:

Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays.

r-rbowtiecuda 1.4.3
Dependencies: gcc@14.3.0 cmake@4.1.3
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://github.com/FranckRICHARD01/RbowtieCuda
Licenses: Modified BSD
Build system: r
Synopsis: An R Wrapper for nvBowtie and nvBWT, a rewritten version of Bowtie2 for cuda
Description:

This package provides an R wrapper for the popular Bowtie2 sequencing read aligner, optimized to run on NVIDIA graphics cards. It includes wrapper functions that enable both genome indexing and alignment to the generated indexes, ensuring high performance and ease of use within the R environment.

r-rvs 1.34.0
Propagated dependencies: r-snpstats@1.60.0 r-r-utils@2.13.0 r-kinship2@1.9.6.2 r-grain@1.4.6 r-genlib@1.1.10
Channel: guix-bioc
Location: guix-bioc/packages/r.scm (guix-bioc packages r)
Home page: https://bioconductor.org/packages/RVS
Licenses: GPL 2
Build system: r
Synopsis: Computes estimates of the probability of related individuals sharing a rare variant
Description:

Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test).

Page: 196979899100126
Total packages: 3017