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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-celegansprobe 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/celegansprobe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type celegans
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 C\_elegans\_probe\_tab.

r-curatedadipochip 1.26.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-experimenthub@3.0.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/MahShaaban/curatedAdipoChIP
Licenses: GPL 3
Build system: r
Synopsis: Curated ChIP-Seq Dataset of MDI-induced Differentiated Adipocytes (3T3-L1)
Description:

This package provides a curated dataset of publicly available ChIP-sequencing of transcription factors, chromatin remodelers and histone modifications in the 3T3-L1 pre-adipocyte cell line. The package document the data collection, pre-processing and processing of the data. In addition to the documentation, the package contains the scripts that was used to generated the data.

r-cnorfuzzy 1.52.0
Propagated dependencies: r-nloptr@2.2.1 r-cellnoptr@1.56.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/CNORfuzzy
Licenses: GPL 2
Build system: r
Synopsis: Addon to CellNOptR: Fuzzy Logic
Description:

This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL).

r-celarefdata 1.28.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/celarefData
Licenses: GPL 3
Build system: r
Synopsis: Processed scRNA data for celaref Vignette - cell labelling by reference
Description:

This experiment data contains some processed data used in the celaref package vignette. These are publically available datasets, that have been processed by celaref package, and can be manipulated further with it.

r-cosmic-67 1.46.0
Propagated dependencies: r-variantannotation@1.56.0 r-summarizedexperiment@1.40.0 r-genomicranges@1.62.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://bioconductor.org/packages/COSMIC.67
Licenses: GPL 3
Build system: r
Synopsis: COSMIC.67
Description:

COSMIC: Catalogue Of Somatic Mutations In Cancer, version 67 (2013-10-24).

r-cyanofilter 1.18.0
Propagated dependencies: r-mrfdepth@1.0.17 r-ggplot2@4.0.1 r-ggally@2.4.0 r-flowdensity@1.44.0 r-flowcore@2.22.0 r-flowclust@3.48.0 r-cytometree@2.0.6 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/fomotis/cyanoFilter
Licenses: Expat
Build system: r
Synopsis: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity
Description:

An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community.

r-crisprviz 1.12.0
Propagated dependencies: r-txdbmaker@1.6.0 r-seqinfo@1.0.0 r-s4vectors@0.48.0 r-iranges@2.44.0 r-gviz@1.54.0 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-crisprdesign@1.12.0 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/crisprViz
Licenses: Expat
Build system: r
Synopsis: Visualization Functions for CRISPR gRNAs
Description:

This package provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework.

r-cotan 2.10.1
Propagated dependencies: r-zeallot@0.2.0 r-withr@3.0.2 r-tidyr@1.3.1 r-tibble@3.3.0 r-stringr@1.6.0 r-singlecellexperiment@1.32.0 r-seurat@5.3.1 r-scales@1.4.0 r-rspectra@0.16-2 r-rlang@1.1.6 r-rfast@2.1.5.2 r-rcolorbrewer@1.1-3 r-proxy@0.4-27 r-parallelly@1.45.1 r-paralleldist@0.2.7 r-matrix@1.7-4 r-ggthemes@5.1.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggdist@3.3.3 r-dplyr@1.1.4 r-dendextend@1.19.1 r-complexheatmap@2.26.0 r-circlize@0.4.16 r-biocsingular@1.26.1 r-assertthat@0.2.1
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/seriph78/COTAN
Licenses: GPL 3
Build system: r
Synopsis: COexpression Tables ANalysis
Description:

Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes.

r-ccimpute 1.12.0
Propagated dependencies: r-summarizedexperiment@1.40.0 r-sparsematrixstats@1.22.0 r-singlecellexperiment@1.32.0 r-rcppeigen@0.3.4.0.2 r-rcpp@1.1.0 r-matrix@1.7-4 r-irlba@2.3.5.1 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: https://github.com/khazum/ccImpute/
Licenses: GPL 3
Build system: r
Synopsis: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8)
Description:

Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities.

r-cycle 1.64.0
Propagated dependencies: r-mfuzz@2.70.0 r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/c.scm (guix-bioc packages c)
Home page: http://cycle.sysbiolab.eu
Licenses: GPL 2
Build system: r
Synopsis: Significance of periodic expression pattern in time-series data
Description:

Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models.

r-dexmadata 1.18.0
Propagated dependencies: r-biobase@2.70.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DExMAdata
Licenses: GPL 2
Build system: r
Synopsis: Data package for DExMA package
Description:

Data objects needed to allSameID() function of DExMA package. There are also some objects that are necessary to be able to apply the examples of the DExMA package, which illustrate package functionality.

r-dspikein 1.0.0
Propagated dependencies: r-xml2@1.5.0 r-treesummarizedexperiment@2.18.0 r-tidyr@1.3.1 r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-scales@1.4.0 r-s4vectors@0.48.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-rcolorbrewer@1.1-3 r-randomforest@4.7-1.2 r-phyloseq@1.54.0 r-phangorn@2.12.1 r-patchwork@1.3.2 r-officer@0.7.1 r-msa@1.42.0 r-microbiome@1.32.0 r-matrixstats@1.5.0 r-limma@3.66.0 r-igraph@2.2.1 r-ggtreeextra@1.20.0 r-ggtree@4.0.1 r-ggstar@1.0.6 r-ggridges@0.5.7 r-ggrepel@0.9.6 r-ggraph@2.2.2 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-ggnewscale@0.5.2 r-ggalluvial@0.12.5 r-flextable@0.9.10 r-edger@4.8.0 r-dplyr@1.1.4 r-deseq2@1.50.2 r-decipher@3.6.0 r-data-table@1.17.8 r-biostrings@2.78.0 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/mghotbi/DspikeIn
Licenses: Expat
Build system: r
Synopsis: Estimating Absolute Abundance from Microbial Spike-in Controls
Description:

This package provides a reproducible and modular workflow for absolute microbial quantification using spike-in controls. Supports both single spike-in taxa and synthetic microbial communities with user-defined spike-in volumes and genome copy numbers. Compatible with phyloseq and TreeSummarizedExperiment (TSE) data structures. The package implements methods for spike-in validation, preprocessing, scaling factor estimation, absolute abundance conversion, bias correction, and normalization. Facilitates downstream statistical analyses with DESeq2', edgeR', and other Bioconductor-compatible methods. Visualization tools are provided via ggplot2', ggtree', and related packages. Includes detailed vignettes, case studies, and function-level documentation to guide users through experimental design, quantification, and interpretation.

r-debrowser 1.38.0
Propagated dependencies: r-sva@3.58.0 r-summarizedexperiment@1.40.0 r-stringi@1.8.7 r-shinyjs@2.1.0 r-shinydashboard@0.7.3 r-shinybs@0.61.1 r-shiny@1.11.1 r-s4vectors@0.48.0 r-reshape2@1.4.5 r-rcurl@1.98-1.17 r-rcolorbrewer@1.1-3 r-plotly@4.11.0 r-pathview@1.50.0 r-org-mm-eg-db@3.22.0 r-org-hs-eg-db@3.22.0 r-limma@3.66.0 r-jsonlite@2.0.0 r-iranges@2.44.0 r-igraph@2.2.1 r-heatmaply@1.6.0 r-harman@1.38.0 r-gplots@3.2.0 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-enrichplot@1.30.3 r-edger@4.8.0 r-dt@0.34.0 r-dose@4.4.0 r-deseq2@1.50.2 r-colourpicker@1.3.0 r-clusterprofiler@4.18.2 r-ashr@2.2-63 r-apeglm@1.32.0 r-annotationdbi@1.72.0 r-annotate@1.88.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/UMMS-Biocore/debrowser
Licenses: FSDG-compatible
Build system: r
Synopsis: Interactive Differential Expresion Analysis Browser
Description:

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

r-drosophila2cdf 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/drosophila2cdf
Licenses: LGPL 2.0+
Build system: r
Synopsis: drosophila2cdf
Description:

This package provides a package containing an environment representing the Drosophila_2.cdf file.

r-dmcfb 1.24.0
Propagated dependencies: r-tibble@3.3.0 r-summarizedexperiment@1.40.0 r-speedglm@0.3-5 r-s4vectors@0.48.0 r-rtracklayer@1.70.0 r-matrixstats@1.5.0 r-mass@7.3-65 r-iranges@2.44.0 r-genomicranges@1.62.0 r-fastdummies@1.7.5 r-data-table@1.17.8 r-biocparallel@1.44.0 r-benchmarkme@1.0.8 r-arm@1.14-4
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DMCFB
Licenses: GPL 3
Build system: r
Synopsis: Differentially Methylated Cytosines via a Bayesian Functional Approach
Description:

DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method.

r-depmap 1.24.0
Propagated dependencies: r-tibble@3.3.0 r-httr2@1.2.1 r-experimenthub@3.0.0 r-dplyr@1.1.4 r-curl@7.0.0 r-biocfilecache@3.0.0 r-annotationhub@4.0.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/depmap
Licenses: Artistic License 2.0
Build system: r
Synopsis: Cancer Dependency Map Data Package
Description:

The depmap package is a data package that accesses datsets from the Broad Institute DepMap cancer dependency study using ExperimentHub. Datasets from the most current release are available, including RNAI and CRISPR-Cas9 gene knockout screens quantifying the genetic dependency for select cancer cell lines. Additional datasets are also available pertaining to the log copy number of genes for select cell lines, protein expression of cell lines as measured by reverse phase protein lysate microarray (RPPA), Transcript Per Million (TPM) data, as well as supplementary datasets which contain metadata and mutation calls for the other datasets found in the current release. The 19Q3 release adds the drug_dependency dataset, that contains cancer cell line dependency data with respect to drug and drug-candidate compounds. The 20Q2 release adds the proteomic dataset that contains quantitative profiling of proteins via mass spectrometry. This package will be updated on a quarterly basis to incorporate the latest Broad Institute DepMap Public cancer dependency datasets. All data made available in this package was generated by the Broad Institute DepMap for research purposes and not intended for clinical use. This data is distributed under the Creative Commons license (Attribution 4.0 International (CC BY 4.0)).

r-deeppincs 1.18.0
Propagated dependencies: r-webchem@1.3.1 r-ttgsea@1.18.0 r-tokenizers@0.3.0 r-tensorflow@2.20.0 r-stringdist@0.9.15 r-reticulate@1.44.1 r-rcdk@3.8.2 r-purrr@1.2.0 r-prroc@1.4 r-matlab@1.0.4.1 r-keras@2.16.0 r-catencoders@0.1.1
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DeepPINCS
Licenses: Artistic License 2.0
Build system: r
Synopsis: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning
Description:

The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences.

r-dandelionr 1.2.0
Propagated dependencies: r-uwot@0.2.4 r-summarizedexperiment@1.40.0 r-spam@2.11-1 r-singlecellexperiment@1.32.0 r-s4vectors@0.48.0 r-rlang@1.1.6 r-rann@2.6.2 r-purrr@1.2.0 r-milor@2.6.0 r-matrix@1.7-4 r-mass@7.3-65 r-igraph@2.2.1 r-destiny@3.24.0 r-bluster@1.20.0 r-biocgenerics@0.56.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://www.github.com/tuonglab/dandelionR/
Licenses: Expat
Build system: r
Synopsis: Single-cell Immune Repertoire Trajectory Analysis in R
Description:

dandelionR is an R package for performing single-cell immune repertoire trajectory analysis, based on the original python implementation. It provides the necessary functions to interface with scRepertoire and a custom implementation of an absorbing Markov chain for pseudotime inference, inspired by the Palantir Python package.

r-dnashaper 1.38.0
Propagated dependencies: r-rcpp@1.1.0 r-genomicranges@1.62.0 r-fields@17.1 r-biostrings@2.78.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DNAshapeR
Licenses: GPL 2
Build system: r
Synopsis: High-throughput prediction of DNA shape features
Description:

DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning.

r-deeptarget 1.4.0
Propagated dependencies: r-stringr@1.6.0 r-readr@2.1.6 r-proc@1.19.0.1 r-ggpubr@0.6.2 r-ggplot2@4.0.1 r-fgsea@1.36.0 r-dplyr@1.1.4 r-depmap@1.24.0 r-biocparallel@1.44.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/DeepTarget
Licenses: GPL 2
Build system: r
Synopsis: Deep characterization of cancer drugs
Description:

This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example.

r-dreamlet 1.8.0
Propagated dependencies: r-zenith@1.12.0 r-variancepartition@1.40.0 r-tidyr@1.3.1 r-summarizedexperiment@1.40.0 r-sparsematrixstats@1.22.0 r-sparsearray@1.10.2 r-singlecellexperiment@1.32.0 r-scattermore@1.2 r-s4vectors@0.48.0 r-s4arrays@1.10.0 r-rlang@1.1.6 r-reshape2@1.4.5 r-remacor@0.0.20 r-rdpack@2.6.4 r-rcpp@1.1.0 r-purrr@1.2.0 r-metafor@4.8-0 r-matrixgenerics@1.22.0 r-matrix@1.7-4 r-mass@7.3-65 r-mashr@0.2.79 r-lme4@1.1-37 r-limma@3.66.0 r-irlba@2.3.5.1 r-iranges@2.44.0 r-gtools@3.9.5 r-gseabase@1.72.0 r-ggrepel@0.9.6 r-ggplot2@4.0.1 r-ggbeeswarm@0.7.2 r-edger@4.8.0 r-dplyr@1.1.4 r-delayedmatrixstats@1.32.0 r-delayedarray@0.36.0 r-data-table@1.17.8 r-broom@1.0.10 r-biocparallel@1.44.0 r-biocgenerics@0.56.0 r-beachmat@2.26.0 r-ashr@2.2-63
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://DiseaseNeurogenomics.github.io/dreamlet
Licenses: Artistic License 2.0
Build system: r
Synopsis: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs
Description:

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

r-drosophila2probe 2.18.0
Propagated dependencies: r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://bioconductor.org/packages/drosophila2probe
Licenses: LGPL 2.0+
Build system: r
Synopsis: Probe sequence data for microarrays of type drosophila2
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 Drosophila\_2\_probe\_tab.

r-dupradar 1.40.0
Propagated dependencies: r-rsubread@2.24.0 r-kernsmooth@2.23-26
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://www.bioconductor.org/packages/dupRadar
Licenses: GPL 3
Build system: r
Synopsis: Assessment of duplication rates in RNA-Seq datasets
Description:

Duplication rate quality control for RNA-Seq datasets.

r-doubletrouble 1.10.0
Propagated dependencies: r-syntenet@1.12.0 r-rlang@1.1.6 r-msa2dist@1.14.0 r-mclust@6.1.2 r-ggplot2@4.0.1 r-genomicranges@1.62.0 r-genomicfeatures@1.62.0 r-biostrings@2.78.0 r-annotationdbi@1.72.0
Channel: guix-bioc
Location: guix-bioc/packages/d.scm (guix-bioc packages d)
Home page: https://github.com/almeidasilvaf/doubletrouble
Licenses: GPL 3
Build system: r
Synopsis: Identification and classification of duplicated genes
Description:

doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.

Total results: 2909