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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

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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-clustall 1.6.0
Propagated dependencies: r-rcolorbrewer@1.1-3 r-pbapply@1.7-4 r-networkd3@0.4.1 r-modeest@2.4.0 r-mice@3.18.0 r-ggplot2@4.0.1 r-fpc@2.2-13 r-foreach@1.5.2 r-flock@0.7 r-factominer@2.12 r-dplyr@1.1.4 r-dosnow@1.0.20 r-complexheatmap@2.26.0 r-clvalid@0.7 r-cluster@2.1.8.1 r-circlize@0.4.16 r-bigstatsr@1.6.2
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ClustAll
Licenses: GPL 2
Build system: r
Synopsis: ClustAll: Data driven strategy to robustly identify stratification of patients within complex diseases
Description:

Data driven strategy to find hidden groups of patients with complex diseases using clinical data. ClustAll facilitates the unsupervised identification of multiple robust stratifications. ClustAll, is able to overcome the most common limitations found when dealing with clinical data (missing values, correlated data, mixed data types).

r-clustirr 1.8.0
Propagated dependencies: r-visnetwork@2.1.4 r-tidyr@1.3.1 r-stringdist@0.9.15 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-posterior@1.6.1 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggforce@0.5.0 r-future-apply@1.20.0 r-future@1.68.0 r-dplyr@1.1.4 r-bh@1.87.0-1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/snaketron/ClustIRR
Licenses: FSDG-compatible
Build system: r
Synopsis: Clustering of immune receptor repertoires
Description:

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

r-cfdnakit 1.8.0
Propagated dependencies: r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-rlang@1.1.6 r-qdnaseq@1.46.0 r-pscbs@0.68.0 r-magrittr@2.0.4 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-dplyr@1.1.4 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/cfdnakit
Licenses: GPL 3
Build system: r
Synopsis: Fragmen-length analysis package from high-throughput sequencing of cell-free DNA (cfDNA)
Description:

This package provides basic functions for analyzing shallow whole-genome sequencing (~0.3X or more) of cell-free DNA (cfDNA). The package basically extracts the length of cfDNA fragments and aids the vistualization of fragment-length information. The package also extract fragment-length information per non-overlapping fixed-sized bins and used it for calculating ctDNA estimation score (CES).

r-cellscape 1.34.0
Propagated dependencies: r-stringr@1.6.0 r-reshape2@1.4.5 r-jsonlite@2.0.0 r-htmlwidgets@1.6.4 r-gtools@3.9.5 r-dplyr@1.1.4
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/cellscape
Licenses: GPL 3
Build system: r
Synopsis: Explores single cell copy number profiles in the context of a single cell tree
Description:

CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus.

r-cftoolsdata 1.8.0
Propagated dependencies: r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/jasminezhoulab/cfToolsData
Licenses: FSDG-compatible
Build system: r
Synopsis: ExperimentHub data for the cfTools package
Description:

The cfToolsData package supplies the data for the cfTools package. It contains two pre-trained deep neural network (DNN) models for the cfSort function. Additionally, it includes the shape parameters of beta distribution characterizing methylation markers associated with four tumor types for the CancerDetector function, as well as the parameters characterizing methylation markers specific to 29 primary human tissue types for the cfDeconvolve function.

r-comethdmr 1.14.0
Propagated dependencies: r-lmertest@3.1-3 r-iranges@2.44.0 r-genomicranges@1.62.0 r-experimenthub@3.0.0 r-bumphunter@1.52.0 r-biocparallel@1.44.0 r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/TransBioInfoLab/coMethDMR
Licenses: GPL 3
Build system: r
Synopsis: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies
Description:

coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously.

r-cleaver 1.48.0
Propagated dependencies: r-s4vectors@0.48.0 r-iranges@2.44.0 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://codeberg.org/sgibb/cleaver/
Licenses: GPL 3+
Build system: r
Synopsis: Cleavage of Polypeptide Sequences
Description:

In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html.

r-curatedbladderdata 1.46.0
Propagated dependencies: r-affy@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/lima1/curatedBladderData
Licenses: Artistic License 2.0
Build system: r
Synopsis: Bladder Cancer Gene Expression Analysis
Description:

The curatedBladderData package provides relevant functions and data for gene expression analysis in patients with bladder cancer.

r-cagefightr 1.30.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-pryr@0.1.6 r-matrix@1.7-4 r-iranges@2.44.0 r-interactionset@1.38.0 r-gviz@1.54.0 r-genomicranges@1.62.0 r-genomicinteractions@1.44.0 r-genomicfiles@1.46.0 r-genomicfeatures@1.62.0 r-genomicalignments@1.46.0 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/MalteThodberg/CAGEfightR
Licenses: FSDG-compatible
Build system: r
Synopsis: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor
Description:

CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.

r-cytopipelinegui 1.8.0
Propagated dependencies: r-shiny@1.11.1 r-plotly@4.11.0 r-ggplot2@4.0.1 r-flowcore@2.22.0 r-cytopipeline@1.10.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://uclouvain-cbio.github.io/CytoPipelineGUI
Licenses: GPL 3
Build system: r
Synopsis: GUI's for visualization of flow cytometry data analysis pipelines
Description:

This package is the companion of the `CytoPipeline` package. It provides GUI's (shiny apps) for the visualization of flow cytometry data analysis pipelines that are run with `CytoPipeline`. Two shiny applications are provided, i.e. an interactive flow frame assessment and comparison tool and an interactive scale transformations visualization and adjustment tool.

r-chipsim 1.64.0
Propagated dependencies: r-xvector@0.50.0 r-shortread@1.68.0 r-iranges@2.44.0 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ChIPsim
Licenses: GPL 2+
Build system: r
Synopsis: Simulation of ChIP-seq experiments
Description:

This package provides a general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments.

r-cnordt 1.52.0
Propagated dependencies: r-cellnoptr@1.56.0 r-abind@1.4-8
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CNORdt
Licenses: GPL 2
Build system: r
Synopsis: Add-on to CellNOptR: Discretized time treatments
Description:

This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge.

r-crisprbowtie 1.14.0
Propagated dependencies: r-stringr@1.6.0 r-seqinfo@1.0.0 r-readr@2.1.6 r-rbowtie@1.50.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-crisprbase@1.14.0 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/crisprVerse/crisprBowtie
Licenses: Expat
Build system: r
Synopsis: Bowtie-based alignment of CRISPR gRNA spacer sequences
Description:

This package provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported.

r-chipanalyser 1.32.0
Propagated dependencies: r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rocr@1.0-11 r-rcpproll@0.3.1 r-rcolorbrewer@1.1-3 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomeinfodb@1.46.0 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocmanager@1.30.27
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ChIPanalyser
Licenses: GPL 3
Build system: r
Synopsis: ChIPanalyser: Predicting Transcription Factor Binding Sites
Description:

ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data.

r-cardinalio 1.8.0
Propagated dependencies: r-s4vectors@0.48.0 r-ontologyindex@2.12 r-matter@2.12.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: http://www.cardinalmsi.org
Licenses: Artistic License 2.0 FSDG-compatible
Build system: r
Synopsis: Read and write mass spectrometry imaging files
Description:

Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging.

r-cn-mops 1.56.0
Propagated dependencies: r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-iranges@2.44.0 r-genomicranges@1.62.0 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://www.bioinf.jku.at/software/cnmops/cnmops.html
Licenses: LGPL 2.0+
Build system: r
Synopsis: cn.mops - Mixture of Poissons for CNV detection in NGS data
Description:

cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++.

r-cghnormaliter 1.64.0
Propagated dependencies: r-cghcall@2.72.0 r-cghbase@1.70.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CGHnormaliter
Licenses: GPL 3+
Build system: r
Synopsis: Normalization of array CGH data with imbalanced aberrations
Description:

Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs).

r-cleanuprnaseq 1.4.0
Propagated dependencies: r-tximport@1.38.1 r-summarizedexperiment@1.40.0 r-seqinfo@1.0.0 r-rsubread@2.24.0 r-rsamtools@2.26.0 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-r6@2.6.1 r-qsmooth@1.26.0 r-pheatmap@1.0.13 r-limma@3.66.0 r-kernsmooth@2.23-26 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-ensembldb@2.34.0 r-edger@4.8.0 r-deseq2@1.50.2 r-bsgenome@1.78.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-annotationfilter@1.34.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CleanUpRNAseq
Licenses: GPL 3
Build system: r
Synopsis: Detect and Correct Genomic DNA Contamination in RNA-seq Data
Description:

RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes.

r-camutqc 1.6.0
Propagated dependencies: r-vcfr@1.15.0 r-tidyr@1.3.1 r-stringr@1.6.0 r-org-hs-eg-db@3.22.0 r-meskit@1.20.0 r-maftools@2.26.0 r-ggplot2@4.0.1 r-dt@0.34.0 r-dplyr@1.1.4 r-data-table@1.17.8 r-clusterprofiler@4.18.2
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/likelet/CaMutQC
Licenses: GPL 3
Build system: r
Synopsis: An R Package for Comprehensive Filtration and Selection of Cancer Somatic Mutations
Description:

CaMutQC is able to filter false positive mutations generated due to technical issues, as well as to select candidate cancer mutations through a series of well-structured functions by labeling mutations with various flags. And a detailed and vivid filter report will be offered after completing a whole filtration or selection section. Also, CaMutQC integrates serveral methods and gene panels for Tumor Mutational Burden (TMB) estimation.

r-clippda 1.60.0
Propagated dependencies: r-statmod@1.5.1 r-scatterplot3d@0.3-44 r-rgl@1.3.31 r-limma@3.66.0 r-lattice@0.22-7 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml
Licenses: FSDG-compatible
Build system: r
Synopsis: package for the clinical proteomic profiling data analysis
Description:

This package provides methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations.

r-consica 2.8.0
Propagated dependencies: r-topgo@2.62.0 r-survival@3.8-3 r-summarizedexperiment@1.40.0 r-sm@2.2-6.0 r-rfast@2.1.5.2 r-pheatmap@1.0.13 r-org-hs-eg-db@3.22.0 r-graph@1.88.0 r-go-db@3.22.0 r-ggplot2@4.0.1 r-fastica@1.2-7 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/consICA
Licenses: Expat
Build system: r
Synopsis: consensus Independent Component Analysis
Description:

consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.

r-crisprbase 1.14.0
Propagated dependencies: r-stringr@1.6.0 r-s4vectors@0.48.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-biostrings@2.78.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/crisprVerse/crisprBase
Licenses: Expat
Build system: r
Synopsis: Base functions and classes for CRISPR gRNA design
Description:

This package provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported.

r-clusterfoldsimilarity 1.6.0
Propagated dependencies: r-singlecellexperiment@1.32.0 r-seuratobject@5.2.0 r-seurat@5.3.1 r-scales@1.4.0 r-reshape2@1.4.5 r-matrix@1.7-4 r-igraph@2.2.1 r-ggplot2@4.0.1 r-ggdendro@0.2.0 r-dplyr@1.1.4 r-cowplot@1.2.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/ClusterFoldSimilarity
Licenses: Artistic License 2.0
Build system: r
Synopsis: Calculate similarity of clusters from different single cell samples using foldchanges
Description:

This package calculates a similarity coefficient using the fold changes of shared features (e.g. genes) among clusters of different samples/batches/datasets. The similarity coefficient is calculated using the dot-product (Hadamard product) of every pairwise combination of Fold Changes between a source cluster i of sample/dataset n and all the target clusters j in sample/dataset m.

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

This package provides a package containing an environment representing the Canine.cdf file.

Total results: 2909