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    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
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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-bsgenome-mmulatta-ucsc-rhemac10 1.4.2
Propagated dependencies: r-bsgenome@1.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Mmulatta.UCSC.rheMac10
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full genome sequences for Macaca mulatta (UCSC version rheMac10)
Description:

Full genome sequences for Macaca mulatta (Rhesus) as provided by UCSC (rheMac10, Feb. 2019) and stored in Biostrings objects.

r-bsgenome-ggallus-ucsc-galgal4 1.4.0
Propagated dependencies: r-bsgenome@1.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Ggallus.UCSC.galGal4
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full genome sequences for Gallus gallus (UCSC version galGal4)
Description:

Full genome sequences for Gallus gallus (Chicken) as provided by UCSC (galGal4, Nov. 2011) and stored in Biostrings objects.

r-bsgenome-btaurus-ucsc-bostau4-masked 1.3.99
Propagated dependencies: r-bsgenome-btaurus-ucsc-bostau4@1.4.0 r-bsgenome@1.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Btaurus.UCSC.bosTau4.masked
Licenses: Artistic License 2.0
Build system: r
Synopsis: Full masked genome sequences for Bos taurus (UCSC version bosTau4)
Description:

Full genome sequences for Bos taurus (Cow) as provided by UCSC (bosTau4, Oct. 2007) and stored in Biostrings objects. The sequences are the same as in BSgenome.Btaurus.UCSC.bosTau4, except that each of them has the 4 following masks on top: (1) the mask of assembly gaps (AGAPS mask), (2) the mask of intra-contig ambiguities (AMB mask), (3) the mask of repeats from RepeatMasker (RM mask), and (4) the mask of repeats from Tandem Repeats Finder (TRF mask). Only the AGAPS and AMB masks are "active" by default.

r-bsgenome-alyrata-jgi-v1 1.0.1
Propagated dependencies: r-bsgenome@1.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Alyrata.JGI.v1
Licenses: Artistic License 2.0
Build system: r
Synopsis: Arabidopsis lyrata full genome (JGI version V1.0)
Description:

Arabidopsis lyrata 8x Release [project ID 4002920] as provided by JGI ( snapshot from March 24, 2011) and stored in Biostrings objects.

r-bridgedbr 2.20.0
Propagated dependencies: r-rjava@1.0-11 r-curl@7.0.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/bridgedb/BridgeDbR
Licenses: AGPL 3
Build system: r
Synopsis: Code for using BridgeDb identifier mapping framework from within R
Description:

Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files.

r-bnem 1.18.0
Propagated dependencies: r-vsn@3.78.0 r-sva@3.58.0 r-snowfall@1.84-6.3 r-rmarkdown@2.30 r-rgraphviz@2.54.0 r-rcolorbrewer@1.1-3 r-mnem@1.26.0 r-matrixstats@1.5.0 r-limma@3.66.0 r-graph@1.88.0 r-flexclust@1.5.0 r-epinem@1.34.0 r-cluster@2.1.8.1 r-cellnoptr@1.56.0 r-biobase@2.70.0 r-binom@1.1-1.1 r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/MartinFXP/bnem/
Licenses: GPL 3
Build system: r
Synopsis: Training of logical models from indirect measurements of perturbation experiments
Description:

bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).

r-blase 1.0.0
Propagated dependencies: r-viridis@0.6.5 r-summarizedexperiment@1.40.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scater@1.38.0 r-rlang@1.1.6 r-patchwork@1.3.2 r-mgcv@1.9-4 r-matrixgenerics@1.22.0 r-matrix@1.7-4 r-ggplot2@4.0.1 r-dplyr@1.1.4 r-boot@1.3-32 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://andrewmccluskey-uog.github.io/blase/
Licenses: GPL 3+
Build system: r
Synopsis: Bulk Linking Analysis for Single-cell Experiments
Description:

BLASE is a method for finding where bulk RNA-seq data lies on a single-cell pseudotime trajectory. It uses a fast and understandable approach based on Spearman correlation, with bootstrapping to provide confidence. BLASE can be used to "date" bulk RNA-seq data, annotate cell types in scRNA-seq, and help correct for developmental phenotype differences in bulk RNA-seq experiments.

r-bsgenome-vvinifera-urgi-iggp8x 0.1
Propagated dependencies: r-bsgenome@1.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BSgenome.Vvinifera.URGI.IGGP8X
Licenses: CC0
Build system: r
Synopsis: Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (IGGP version 8X)
Description:

Full reference nuclear genome sequences for Vitis vinifera subsp. vinifera PN40024 (derived from Pinot Noir and close to homozygosity after 6-9 rounds of selfing) as assembled by the IGGP (version 8X) and available at the URGI (INRA). More details in Jaillon et al (Nature, 2007).

r-bcseq 1.32.0
Propagated dependencies: r-rcpp@1.1.0 r-matrix@1.7-4 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/jl354/bcSeq
Licenses: GPL 2+
Build system: r
Synopsis: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens
Description:

This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading.

r-barley1cdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/barley1cdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: barley1cdf
Description:

This package provides a package containing an environment representing the Barley1.CDF file.

r-bert 1.6.0
Propagated dependencies: r-sva@3.58.0 r-summarizedexperiment@1.40.0 r-logging@0.10-108 r-limma@3.66.0 r-janitor@2.2.1 r-iterators@1.0.14 r-invgamma@1.2 r-foreach@1.5.2 r-comprehenr@0.6.10 r-cluster@2.1.8.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/HSU-HPC/BERT/
Licenses: GPL 3
Build system: r
Synopsis: High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT)
Description:

This package provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees.

r-bloodcancermultiomics2017 1.30.0
Propagated dependencies: r-tibble@3.3.0 r-survival@3.8-3 r-summarizedexperiment@1.40.0 r-scales@1.4.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-ipflasso@1.1 r-gtable@0.3.6 r-glmnet@4.1-10 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-dplyr@1.1.4 r-devtools@2.4.6 r-deseq2@1.50.2 r-biobase@2.70.0 r-beeswarm@0.4.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://bioconductor.org/packages/BloodCancerMultiOmics2017
Licenses: LGPL 3+
Build system: r
Synopsis: "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al. - experimental data and complete analysis
Description:

The package contains data of the Primary Blood Cancer Encyclopedia (PACE) project together with a complete executable transcript of the statistical analysis and reproduces figures presented in the paper "Drug-perturbation-based stratification of blood cancer" by Dietrich S, Oleś M, Lu J et al., J. Clin. Invest. (2018) 128(1):427-445. doi:10.1172/JCI93801.

r-batchcorr 1.0.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-reshape@0.8.10 r-mclust@6.1.2 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/b.scm (guix-bioc packages b)
Home page: https://github.com/MetaboComp/batchCorr
Licenses: GPL 2
Build system: r
Synopsis: Within And Between Batch Correction Of LC-MS Metabolomics Data
Description:

From the perspective of metabolites as the continuation of the central dogma of biology, metabolomics provides the closest link to many phenotypes of interest. This makes metabolomics research promising in teasing apart the complexities of living systems. However, due to experimental reasons, the data includes non-biological variation which limits quality and reproducibility, especially if the data is obtained from several batches. The batchCorr package reduces unwanted variation by way of between-batch alignment, within-batch drift correction and between-batch normalization using batch-specific quality control samples and long-term reference QC samples. Please see the associated article for more thorough descriptions of algorithms.

r-compepitools 1.44.0
Propagated dependencies: r-xvector@0.50.0 r-topgo@2.62.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-methylpipe@1.44.0 r-iranges@2.44.0 r-gplots@3.2.0 r-go-db@3.22.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/compEpiTools
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Tools for computational epigenomics
Description:

This package provides tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples.

r-cliprofiler 1.16.0
Propagated dependencies: r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-dplyr@1.1.4 r-bsgenome@1.78.0 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/Codezy99/cliProfiler
Licenses: Artistic License 2.0
Build system: r
Synopsis: package for the CLIP data visualization
Description:

An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results.

r-curatedpcadata 1.6.0
Propagated dependencies: r-s4vectors@0.48.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-raggedexperiment@1.34.0 r-multiassayexperiment@1.36.1 r-experimenthub@3.0.0 r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/Syksy/curatedPCaData
Licenses: FSDG-compatible
Build system: r
Synopsis: Curated Prostate Cancer Data
Description:

The package curatedPCaData offers a selection of annotated prostate cancer datasets featuring multiple omics, manually curated metadata, and derived downstream variables. The studies are offered as MultiAssayExperiment (MAE) objects via ExperimentHub, and comprise of clinical characteristics tied to gene expression, copy number alteration and somatic mutation data. Further, downstream features computed from these multi-omics data are offered. Multiple vignettes help grasp characteristics of the various studies and provide example exploratory and meta-analysis of leveraging the multiple studies provided here-in.

r-compcoder 1.46.0
Propagated dependencies: r-vioplot@0.5.1 r-stringr@1.6.0 r-sm@2.2-6.0 r-shinydashboard@0.7.3 r-shiny@1.11.1 r-rocr@1.0-11 r-rmarkdown@2.30 r-phylolm@2.6.5 r-modeest@2.4.0 r-matrixstats@1.5.0 r-mass@7.3-65 r-markdown@2.0 r-limma@3.66.0 r-lattice@0.22-7 r-knitr@1.50 r-kernsmooth@2.23-26 r-gtools@3.9.5 r-gplots@3.2.0 r-ggplot2@4.0.1 r-edger@4.8.0 r-catools@1.18.3 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/csoneson/compcodeR
Licenses: GPL 2+
Build system: r
Synopsis: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods
Description:

This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package.

r-cntools 1.66.0
Propagated dependencies: r-genefilter@1.92.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CNTools
Licenses: LGPL 2.0+
Build system: r
Synopsis: Convert segment data into a region by sample matrix to allow for other high level computational analyses
Description:

This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data.

r-clariomdhumantranscriptcluster-db 8.8.0
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/clariomdhumantranscriptcluster.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix clariomdhuman annotation data (chip clariomdhumantranscriptcluster)
Description:

Affymetrix clariomdhuman annotation data (chip clariomdhumantranscriptcluster) assembled using data from public repositories.

r-cellmig 1.0.0
Propagated dependencies: r-stanheaders@2.32.10 r-scales@1.4.0 r-rstantools@2.5.0 r-rstan@2.32.7 r-reshape2@1.4.5 r-rcppparallel@5.1.11-1 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-patchwork@1.3.2 r-ggtree@4.0.1 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-bh@1.87.0-1 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/snaketron/cellmig
Licenses: FSDG-compatible
Build system: r
Synopsis: Uncertainty-aware quantitative analysis of high-throughput live cell migration data
Description:

High-throughput cell imaging facilitates the analysis of cell migration across many wells treated under different biological conditions. These workflows generate considerable technical noise and biological variability, and therefore technical and biological replicates are necessary, leading to large, hierarchically structured datasets, i.e., cells are nested within technical replicates that are nested within biological replicates. Current statistical analyses of such data usually ignore the hierarchical structure of the data and fail to explicitly quantify uncertainty arising from technical or biological variability. To address this gap, we present cellmig, an R package implementing Bayesian hierarchical models for migration analysis. cellmig quantifies condition- specific velocity changes (e.g., drug effects) while modeling nested data structures and technical artifacts. It further enables synthetic data generation for experimental design optimization.

r-consensusde 1.28.0
Propagated dependencies: r-txdb-dmelanogaster-ucsc-dm3-ensgene@3.2.2 r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-ruvseq@1.44.0 r-rsamtools@2.26.0 r-rcolorbrewer@1.1-3 r-pcamethods@2.2.0 r-org-hs-eg-db@3.22.0 r-limma@3.66.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-ensembldb@2.34.0 r-ensdb-hsapiens-v86@2.99.0 r-edger@4.8.0 r-edaseq@2.44.0 r-deseq2@1.50.2 r-dendextend@1.19.1 r-data-table@1.17.8 r-biostrings@2.78.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-biobase@2.70.0 r-annotationdbi@1.72.0 r-airway@1.30.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/consensusDE
Licenses: GPL 3
Build system: r
Synopsis: RNA-seq analysis using multiple algorithms
Description:

This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation.

r-cocoa 2.24.0
Propagated dependencies: r-tidyr@1.3.1 r-simplecache@0.4.2 r-s4vectors@0.48.0 r-mira@1.32.0 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-fitdistrplus@1.2-4 r-data-table@1.17.8 r-complexheatmap@2.26.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: http://code.databio.org/COCOA/
Licenses: GPL 3
Build system: r
Synopsis: Coordinate Covariation Analysis
Description:

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

r-clustersignificance 1.38.0
Propagated dependencies: r-scatterplot3d@0.3-44 r-rcolorbrewer@1.1-3 r-princurve@2.1.6 r-pracma@2.4.6
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/jasonserviss/ClusterSignificance/
Licenses: GPL 3
Build system: r
Synopsis: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data
Description:

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.

r-cfdnapro 1.16.0
Propagated dependencies: r-tibble@3.3.0 r-stringr@1.6.0 r-rsamtools@2.26.0 r-rlang@1.1.6 r-quantmod@0.4.28 r-plyranges@1.30.1 r-magrittr@2.0.4 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.0 r-dplyr@1.1.4 r-bsgenome-hsapiens-ucsc-hg38@1.4.5 r-bsgenome-hsapiens-ucsc-hg19@1.4.3 r-bsgenome-hsapiens-ncbi-grch38@1.3.1000 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/hw538/cfDNAPro
Licenses: GPL 3
Build system: r
Synopsis: cfDNAPro extracts and Visualises biological features from whole genome sequencing data of cell-free DNA
Description:

cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization.

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