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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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r-nugomm1a520177probe 3.4.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nugomm1a520177probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type nugomm1a520177
Description:

This package was automatically created by package AnnotationForge version 1.11.20. The probe sequence data was obtained from http://www.affymetrix.com.

r-nugohs1a520180probe 3.4.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nugohs1a520180probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type nugohs1a520180
Description:

This package was automatically created by package AnnotationForge version 1.11.20. The probe sequence data was obtained from http://www.affymetrix.com.

r-nugohs1a520180-db 3.4.0
Propagated dependencies: r-org-hs-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nugohs1a520180.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix nugohs1a520180 annotation data (chip nugohs1a520180)
Description:

Affymetrix nugohs1a520180 annotation data (chip nugohs1a520180) assembled using data from public repositories.

r-nugohs1a520180cdf 3.4.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nugohs1a520180cdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: nugohs1a520180cdf
Description:

This package provides a package containing an environment representing the NuGO_Hs1a520180.cdf file.

r-norce 1.22.0
Propagated dependencies: r-tidyr@1.3.1 r-summarizedexperiment@1.40.0 r-stringr@1.6.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rwikipathways@1.30.0 r-rtracklayer@1.70.0 r-rsqlite@2.4.4 r-reshape2@1.4.5 r-readr@2.1.6 r-reactome-db@1.94.0 r-rcurl@1.98-1.17 r-png@0.1-8 r-keggrest@1.50.0 r-iranges@2.44.0 r-igraph@2.2.1 r-go-db@3.22.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-dplyr@1.1.4 r-dbplyr@2.5.1 r-dbi@1.2.3 r-biomart@2.66.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NoRCE
Licenses: Expat
Build system: r
Synopsis: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
Description:

While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.

r-netpathminer 1.46.0
Dependencies: libxml2@2.14.6 libxml2@2.14.6 libsbml@5.20.5
Propagated dependencies: r-igraph@2.2.1
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/ahmohamed/NetPathMiner
Licenses: GPL 2+
Build system: r
Synopsis: NetPathMiner for Biological Network Construction, Path Mining and Visualization
Description:

NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation.

r-nanotube 1.16.0
Propagated dependencies: r-reshape@0.8.10 r-limma@3.66.0 r-ggplot2@4.0.1 r-fgsea@1.36.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NanoTube
Licenses: FSDG-compatible
Build system: r
Synopsis: An Easy Pipeline for NanoString nCounter Data Analysis
Description:

NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.

r-nondetects 2.40.0
Propagated dependencies: r-mvtnorm@1.3-3 r-limma@3.66.0 r-htqpcr@1.64.0 r-biobase@2.70.0 r-arm@1.14-4
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nondetects
Licenses: GPL 3
Build system: r
Synopsis: Non-detects in qPCR data
Description:

This package provides methods to model and impute non-detects in the results of qPCR experiments.

r-nanoporernaseq 1.20.0
Propagated dependencies: r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/GoekeLab/NanoporeRNASeq
Licenses: FSDG-compatible
Build system: r
Synopsis: Nanopore RNA-Seq Example data
Description:

The NanoporeRNASeq package contains long read RNA-Seq data generated using Oxford Nanopore Sequencing. The data consists of 6 samples from two human cell lines (K562 and MCF7) that were generated by the SG-NEx project. Each of these cell lines has three replicates, with 1 direct RNA sequencing data and 2 cDNA sequencing data. Reads are aligned to chromosome 22 (Grch38) and stored as bam files. The original data is from the SG-NEx project.

r-nipalsmcia 1.8.1
Propagated dependencies: r-summarizedexperiment@1.40.0 r-scales@1.4.0 r-rspectra@0.16-2 r-rlang@1.1.6 r-pracma@2.4.6 r-multiassayexperiment@1.36.1 r-ggplot2@4.0.1 r-fgsea@1.36.0 r-dplyr@1.1.4 r-complexheatmap@2.26.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/Muunraker/nipalsMCIA
Licenses: GPL 3
Build system: r
Synopsis: Multiple Co-Inertia Analysis via the NIPALS Method
Description:

Computes Multiple Co-Inertia Analysis (MCIA), a dimensionality reduction (jDR) algorithm, for a multi-block dataset using a modification to the Nonlinear Iterative Partial Least Squares method (NIPALS) proposed in (Hanafi et. al, 2010). Allows multiple options for row- and table-level preprocessing, and speeds up computation of variance explained. Vignettes detail application to bulk- and single cell- multi-omics studies.

r-ncgtw 1.24.0
Propagated dependencies: r-xcms@4.8.0 r-rcpp@1.1.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/ncGTW
Licenses: GPL 2
Build system: r
Synopsis: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection
Description:

The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not.

r-nucler 2.42.0
Propagated dependencies: r-shortread@1.68.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-rsamtools@2.26.0 r-magrittr@2.0.4 r-iranges@2.44.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-dplyr@1.1.4 r-biostrings@2.78.0 r-biocgenerics@0.56.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nucleR
Licenses: LGPL 3+
Build system: r
Synopsis: Nucleosome positioning package for R
Description:

Nucleosome positioning for Tiling Arrays and NGS experiments.

r-nullranges 1.16.3
Propagated dependencies: r-seqinfo@1.0.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rlang@1.1.6 r-progress@1.2.3 r-plyranges@1.30.1 r-iranges@2.44.0 r-interactionset@1.38.0 r-ggridges@0.5.7 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-data-table@1.17.8
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://nullranges.github.io/nullranges
Licenses: GPL 3
Build system: r
Synopsis: Generation of null ranges via bootstrapping or covariate matching
Description:

Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.

r-notamestats 1.0.0
Propagated dependencies: r-tidyr@1.3.1 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-notame@1.0.1 r-dplyr@1.1.4 r-broom@1.0.10 r-biocparallel@1.44.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/hanhineva-lab/notameStats
Licenses: Expat
Build system: r
Synopsis: Workflow for non-targeted LC-MS metabolic profiling
Description:

This package provides univariate and multivariate statistics for feature prioritization in untargeted LC-MS metabolomics research.

r-nxtirfdata 1.16.0
Propagated dependencies: r-rtracklayer@1.70.0 r-r-utils@2.13.0 r-experimenthub@3.0.0 r-biocfilecache@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/alexchwong/NxtIRFdata
Licenses: Expat
Build system: r
Synopsis: Data for NxtIRF
Description:

NxtIRFdata is a companion package for SpliceWiz, an interactive analysis and visualization tool for alternative splicing quantitation (including intron retention) for RNA-seq BAM files. NxtIRFdata contains Mappability files required for the generation of human and mouse references. NxtIRFdata also contains a synthetic genome reference and example BAM files used to demonstrate SpliceWiz's functionality. BAM files are based on 6 samples from the Leucegene dataset provided by NCBI Gene Expression Omnibus under accession number GSE67039.

r-nethet 1.42.0
Propagated dependencies: r-network@1.19.0 r-mvtnorm@1.3-3 r-multtest@2.66.0 r-mclust@6.1.2 r-limma@3.66.0 r-icsnp@1.1-2 r-huge@1.3.5 r-gsa@1.03.3 r-glmnet@4.1-10 r-glasso@1.11 r-ggplot2@4.0.1 r-ggm@2.5.2 r-genenet@1.2.17 r-compquadform@1.4.4
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nethet
Licenses: GPL 2
Build system: r
Synopsis: bioconductor package for high-dimensional exploration of biological network heterogeneity
Description:

Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).

r-nanostringdiff 1.40.0
Propagated dependencies: r-rcpp@1.1.0 r-matrixstats@1.5.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NanoStringDiff
Licenses: GPL 2+ GPL 3+
Build system: r
Synopsis: Differential Expression Analysis of NanoString nCounter Data
Description:

This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized.

r-nadfinder 1.34.0
Propagated dependencies: r-trackviewer@1.46.0 r-summarizedexperiment@1.40.0 r-signal@1.8-1 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-rsamtools@2.26.0 r-metap@1.12 r-limma@3.66.0 r-iranges@2.44.0 r-genomicranges@1.62.0 r-genomicalignments@1.46.0 r-genomeinfodb@1.46.0 r-empiricalbrownsmethod@1.38.0 r-csaw@1.44.0 r-corrplot@0.95 r-biocgenerics@0.56.0 r-baseline@1.3-7 r-atacseqqc@1.34.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NADfinder
Licenses: GPL 2+
Build system: r
Synopsis: Call wide peaks for sequencing data
Description:

Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.

r-netboost 2.18.1
Dependencies: perl@5.36.0 gzip@1.14 bash@5.2.37
Propagated dependencies: r-wgcna@1.73 r-rcppparallel@5.1.11-1 r-rcpp@1.1.0 r-r-utils@2.13.0 r-impute@1.84.0 r-dynamictreecut@1.63-1 r-colorspace@2.1-2
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/release/bioc/html/netboost.html
Licenses: GPL 3
Build system: r
Synopsis: Network Analysis Supported by Boosting
Description:

Boosting supported network analysis for high-dimensional omics applications.

r-netactivitydata 1.12.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NetActivityData
Licenses: Expat
Build system: r
Synopsis: Data required for getting the gene set scores with NetActivity package
Description:

This package contains the weights from pre-trained shallow sparsely-connected autoencoders. This data is required for getting the gene set scores with NetActivity package.

r-nupop 2.18.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/NuPoP
Licenses: GPL 2
Build system: r
Synopsis: An R package for nucleosome positioning prediction
Description:

NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP.

r-nugomm1a520177-db 3.4.0
Propagated dependencies: r-org-mm-eg-db@3.22.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://bioconductor.org/packages/nugomm1a520177.db
Licenses: Artistic License 2.0
Build system: r
Synopsis: Affymetrix nugomm1a520177 annotation data (chip nugomm1a520177)
Description:

Affymetrix nugomm1a520177 annotation data (chip nugomm1a520177) assembled using data from public repositories.

r-nanotubes 1.26.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/MalteThodberg/nanotubes
Licenses: GPL 3
Build system: r
Synopsis: Mouse nanotube CAGE data
Description:

Cap Analysis of Gene Expression (CAGE) data from "Identification of Gene Transcription Start Sites and Enhancers Responding to Pulmonary Carbon Nanotube Exposure in Vivo" by Bornholdt et al. supplied as CAGE Transcription Start Sites (CTSSs).

r-nbamseq 1.26.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-s4vectors@0.48.0 r-mgcv@1.9-4 r-genefilter@1.92.0 r-deseq2@1.50.2 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/reese3928/NBAMSeq
Licenses: GPL 2
Build system: r
Synopsis: Negative Binomial Additive Model for RNA-Seq Data
Description:

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.

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