Sets of identifiers of interest can be directly uploaded in text format, pasted in a text field or interactively derived from gene networks. The plugin automatically recognizes a variety of identifiers for genes, proteins or miRNAs that can be updated with the latest NCBI information.
Importantly, this enrichment can be used within ClueGO networks of pathways. Newly enriched genes already known to be associated with a pathway will be automatically linked to it. Another original feature of CluePedia expands ClueGO terms into nested networks. Like this, a pathway can be investigated in detail to reveal how known gene interrelations are modulated within the experimental context used, and which could be the newly associated genes/miRNAs.
Note: For others coming across this, the issue may also be that you're using plugins/preset from Babel 6 on Babel 7. This may be hard to notice if you're using a third-party Babel preset since the versions of the presets may not match the version of Babel itself.
aggregateBioVar For single cellRNA-seq data collected from more than one subject (e.g. biologicalsample or technical replicates), this package contains tools tosummarize single cell gene expression profiles at the level ofsubject. A SingleCellExperiment object is taken as input andconverted to a list of SummarizedExperiment objects, where eachlist element corresponds to an assigned cell type. TheSummarizedExperiment objects contain aggregate gene-by-subjectcount matrices and inter-subject column metadata for individualsubjects that can be processed using downstream bulk RNA-seq tools.
CytoTree A trajectory inference toolkit forflow and mass cytometry data. CytoTree is a valuable tool to builda tree-shaped trajectory using flow and mass cytometry data. Theapplication of CytoTree ranges from clustering and dimensionalityreduction to trajectory reconstruction and pseudotime estimation.It offers complete analyzing workflow for flow and mass cytometrydata.
GSEAmining Gene Set Enrichment Analysis isa very powerful and interesting computational method that allows aneasy correlation between differential expressed genes andbiological processes. Unfortunately, although it was designed tohelp researchers to interpret gene expression data it can generatehuge amounts of results whose biological meaning can be difficultto interpret. Many available tools rely on the hierarchicallystructured Gene Ontology (GO) classification to reduce reundandcyin the results. However, due to the popularity of GSEA many moregene set collections, such as those in the Molecular SignaturesDatabase are emerging. Since these collections are not organized asthose in GO, their usage for GSEA do not always give astraightforward answer or, in other words, getting all themeaninful information can be challenging with the currentlyavailable tools. For these reasons, GSEAmining was born to be aneasy tool to create reproducible reports to help researchers makebiological sense of GSEA outputs. Given the results of GSEA,GSEAmining clusters the different gene sets collections based onthe presence of the same genes in the leadind edge (core) subset.Leading edge subsets are those genes that contribute most to theenrichment score of each collection of genes or gene sets. For thisreason, gene sets that participate in similar biological processesshould share genes in common and in turn cluster together. Afterthat, GSEAmining is able to identify and represent for eachcluster: - The most enriched terms in the names of gene sets (aswordclouds) - The most enriched genes in the leading edge subsets(as bar plots). In each case, positive and negative enrichments areshown in different colors so it is easy to distinguish biologicalprocesses or genes that may be of interest in that particularstudy.
GWENA The development of high-throughputsequencing led to increased use of co-expression analysis to gobeyong single feature (i.e. gene) focus. We propose GWENA (GeneWhole co-Expression Network Analysis) , a tool designed to performgene co-expression network analysis and explore the results in asingle pipeline. It includes functional enrichment of modules ofco-expressed genes, phenotypcal association, topological analysisand comparison of networks configuration between conditions.
Herper Many tools for data analysis are notavailable in R, but are present in public repositories like conda.The Herper package provides a comprehensive set of functions tointeract with the conda package managament system. With Herperusers can install, manage and run conda packages from the comfortof their R session. Herper also provides an ad-hoc approach tohandling external system requirements for R packages. For peopledeveloping packages with python conda dependencies we recommendusing basilisk( )to internally support these system requirments pre-hoc.
ILoReg ILoReg is a tool for identification ofcell populations from scRNA-seq data. In particular, ILoReg isuseful for finding cell populations with subtle transcriptomicdifferences. The method utilizes a self-supervised learning method,called Iteratitive Clustering Projection (ICP), to find clusterprobabilities, which are used in noise reduction prior to PCA andthe subsequent hierarchical clustering and t-SNE steps.Additionally, functions for differential expression analysis tofind gene markers for the populations and gene expressionvisualization are provided.
ISAnalytics In gene therapy, stem cellsare modified using viral vectors to deliver the therapeutictransgene and replace functional properties since the geneticmodification is stable and inherited in all cell progeny. Theretrieval and mapping of the sequences flanking the virus-host DNAjunctions allows the identification of insertion sites (IS),essential for monitoring the evolution of genetically modifiedcells in vivo. A comprehensive toolkit for the analysis of IS isrequired to foster clonal trackign studies and supporting theassessment of safety and long term efficacy in vivo. This packageis aimed at (1) supporting automation of IS workflow, (2)performing base and advance analysis for IS tracking (clonalabundance, clonal expansions and statistics for insertionalmutagenesis, etc.), (3) providing basic biology insights oftransduced stem cells in vivo.
MesKit MesKit provides commonly used analysisand visualization modules based on mutational data generated bymulti-region sequencing (MRS). This package allows to decipher ITH,infer metastatic routes as well as uncover the underlying processof mutagenesis. Shiny application was also developed for a need ofGUI-based analysis. As a handy tool, MesKit can facilitate theunderstanding of cancer cell evolution and its relevance to cancertherapeutics.
msImpute MsImpute is a package for imputationof peptide intensity in proteomics experiments. It additionallycontains tools for MAR/MNAR diagnosis and assessment of distortionsto the probability distribution of the data post imputation.Currently, msImpute completes missing values by low-rankapproximation of the underlying data matrix.
MSstatsPTM MSstatsPTM provides generalstatistical methods for quantitative characterization ofpost-translational modifications (PTMs). Typically, the analysisinvolves the quantification of PTM sites (i.e., modified residues)and their corresponding proteins, as well as the integration of thequantification results. MSstatsPTM provides functions forsummarization, estimation of PTM site abundance, and detection ofchanges in PTMs across experimental conditions.
musicatk Mutational signatures arecarcinogenic exposures or aberrant cellular processes that cancause alterations to the genome. We created musicatk (MUtationalSIgnature Comprehensive Analysis ToolKit) to address shortcomingsin versatility and ease of use in other pre-existing computationaltools. Although many different types of mutational data have beengenerated, current software packages do not have a flexibleframework to allow users to mix and match different types ofmutations in the mutational signature inference process. Musicatkenables users to count and combine multiple mutation types,including SBS, DBS, and indels. Musicatk calculates replicationstrand, transcription strand and combinations of these featuresalong with discovery from unique and proprietary genomic featureassociated with any mutation type. Musicatk also implements severalmethods for discovery of new signatures as well as methods to inferexposure given an existing set of signatures. Musicatk providesfunctions for visualization and downstream exploratory analysisincluding the ability to compare signatures between cohorts andfind matching signatures in COSMIC V2 or COSMIC V3.
NanoMethViz NanoMethViz is a toolkit forvisualising methylation data from Oxford Nanopore sequencing. Itcan be used to explore methylation patterns from reads derived fromOxford Nanopore direct DNA sequencing with methylation called bycallers including nanopolish, f5c and megalodon. The plots in thispackage allow the visualisation of methylation profiles aggregatedover experimental groups and across classes of genomic features.
ncRNAtools ncRNAtools provides a set ofbasic tools for handling and analyzing non-coding RNAs. Theseinclude tools to access the RNAcentral database and to predict andvisualize the secondary structure of non-coding RNAs. The packagealso provides tools to read, write and interconvert the fileformats most commonly used for representing such secondarystructures.
PhosR PhosR is a package for the comprenhensiveanalysis of phosphoproteomic data. There are two major componentsto PhosR: processing and downstream analysis. PhosR consists ofvarious processing tools for phosphoproteomics data includingfiltering, imputation, normalisation, and functional analysis forinferring active kinases and signalling pathways.
SCATE SCATE is a software tool for extractingand enhancing the sparse and discrete Single-cell ATAC-seq Signal.Single-cell sequencing assay for transposase-accessible chromatin(scATAC-seq) is the state-of-the-art technology for analyzinggenome-wide regulatory landscapes in single cells. Single-cellATAC-seq data are sparse and noisy, and analyzing such data ischallenging. Existing computational methods cannot accuratelyreconstruct activities of individual cis-regulatory elements (CREs)in individual cells or rare cell subpopulations. SCATE wasdeveloped to adaptively integrate information from co-activatedCREs, similar cells, and publicly available regulome data andsubstantially increase the accuracy for estimating activities ofindividual CREs. We demonstrate that SCATE can be used to betterreconstruct the regulatory landscape of a heterogeneous sample. 2b1af7f3a8