orthogene - Gene mapping made easy
`orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species.
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geneticscomparativegenomicspreprocessingphylogeneticstranscriptomicsgeneexpressionanimal-modelsbioconductorbioconductor-packagebioinformaticsbiomedicinecomparative-genomicsevolutionary-biologygenesgenomicsontologiestranslational-research
9.34 score 58 stars 3 dependents 111 scripts 1.4k downloadsrworkflows - Test, Document, Containerise, and Deploy R Packages
Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers. rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action.
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softwareworkflowmanagementbioconductorcontainerscontinuous-integrationdockerdockerhubgithub-actionsreproducibilityworkflows
7.75 score 90 stars 5 scripts 408 downloadsEpiCompare - Comparison, Benchmarking & QC of Epigenomic Datasets
EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS.
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epigeneticsgeneticsqualitycontrolchipseqmultiplecomparisonfunctionalgenomicsatacseqdnaseseqbenchmarkbenchmarkingbioconductorbioconductor-packagecomparisonhtmlinteractive-reporting
7.53 score 19 stars 42 scripts 442 downloadsewceData - The ewceData package provides reference data required for ewce
This package provides reference data required for ewce. Expression Weighted Celltype Enrichment (EWCE) is used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses.
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experimentdataexperimenthubexpressiondatagenomeproteomemicroarraydatasequencingdatasinglecelldatarnaseqdatadatapkg
5.97 score 1 dependents 62 scriptsMungeSumstats - Standardise summary statistics from GWAS
The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences.
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snpwholegenomegeneticscomparativegenomicsgenomewideassociationgenomicvariationpreprocessing
5.80 score 4 stars 182 scripts 1.2k downloadsMotifPeeker - Benchmarking Epigenomic Profiling Methods Using Motif Enrichment
MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. Motif Discovery Enrichment Analysis) Statistics for the frequency of ab-initio discovered motifs enriched in the datasets and compared with known motifs.
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epigeneticsgeneticsqualitycontrolchipseqmultiplecomparisonfunctionalgenomicsmotifdiscoverysequencematchingsoftwarealignmentbioconductorbioconductor-packagechip-seqepigenomicsinteractive-reportmotif-enrichment-analysis
5.56 score 3 stars 7 scripts 248 downloads