Showing 68 of total 68 results (show query)
bioc
miRBaseConverter:A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions
A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends.
Maintained by Taosheng Xu Taosheng Xu. Last updated 5 months ago.
70.4 match 1 stars 6.50 score 70 scriptsbioc
mirTarRnaSeq:mirTarRnaSeq
mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments.
Maintained by Mercedeh Movassagh. Last updated 5 months ago.
mirnaregressionsoftwaresequencingsmallrnatimecoursedifferentialexpression
103.1 match 4.00 score 9 scriptsbioc
isomiRs:Analyze isomiRs and miRNAs from small RNA-seq
Characterization of miRNAs and isomiRs, clustering and differential expression.
Maintained by Lorena Pantano. Last updated 5 months ago.
mirnarnaseqdifferentialexpressionclusteringimmunooncologyanalyze-isomirsbioconductorisomirs
47.9 match 8 stars 7.09 score 43 scriptsbioc
miRSM:Inferring miRNA sponge modules in heterogeneous data
The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsclusteringgenesetenrichmentmicroarraysoftwaregeneregulationgenetargetcernamirnamirna-spongemirna-targetsmodulesopenjdk
53.7 match 4 stars 5.68 score 5 scriptsbioc
MIRit:Integrate microRNA and gene expression to decipher pathway complexity
MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.
Maintained by Jacopo Ronchi. Last updated 18 hours ago.
softwaregeneregulationnetworkenrichmentnetworkinferenceepigeneticsfunctionalgenomicssystemsbiologynetworkpathwaysgeneexpressiondifferentialexpressionmirnamirna-mrna-interactionmirna-seqmirnaseq-analysiscpp
69.8 match 4.00 score 2 scriptsbioc
miRLAB:Dry lab for exploring miRNA-mRNA relationships
Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses.
Maintained by Thuc Duy Le. Last updated 5 months ago.
mirnageneexpressionnetworkinferencenetwork
57.3 match 4.72 score 11 scriptsbioc
SPONGE:Sparse Partial Correlations On Gene Expression
This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Maintained by Markus List. Last updated 5 months ago.
geneexpressiontranscriptiongeneregulationnetworkinferencetranscriptomicssystemsbiologyregressionrandomforestmachinelearning
41.7 match 5.36 score 38 scripts 1 dependentsbioc
miRspongeR:Identification and analysis of miRNA sponge regulation
This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsnetworkenrichmentsurvivalmicroarraysoftwaresinglecellspatialrnaseqcernamirnasponge
36.8 match 5 stars 5.88 score 8 scriptsbioc
PanomiR:Detection of miRNAs that regulate interacting groups of pathways
PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data.
Maintained by Pourya Naderi. Last updated 5 months ago.
geneexpressiongenesetenrichmentgenetargetmirnapathways
36.2 match 3 stars 4.89 score 13 scriptsbioc
scanMiR:scanMiR
A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).
Maintained by Pierre-Luc Germain. Last updated 5 months ago.
mirnasequencematchingalignment
26.2 match 5.89 score 52 scripts 1 dependentsbioc
ceRNAnetsim:Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA)
This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables.
Maintained by Selcen Ari Yuka. Last updated 5 months ago.
networkinferencesystemsbiologynetworkgraphandnetworktranscriptomicscernamirnanetwork-biologynetwork-simulatortcgatidygraphtidyverse
26.5 match 4 stars 5.76 score 12 scriptsbioc
GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC
This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.
Maintained by Ruidong Li. Last updated 5 months ago.
immunooncologygeneexpressiondifferentialexpressiongeneregulationgenetargetnetworkinferencesurvivalvisualizationgenesetenrichmentnetworkenrichmentnetworkrnaseqgokegg
25.9 match 5.64 score 44 scripts
cran
miRetrieve:miRNA Text Mining in Abstracts
Providing tools for microRNA (miRNA) text mining. miRetrieve summarizes miRNA literature by extracting, counting, and analyzing miRNA names, thus aiming at gaining biological insights into a large amount of text within a short period of time. To do so, miRetrieve uses regular expressions to extract miRNAs and tokenization to identify meaningful miRNA associations. In addition, miRetrieve uses the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020) "miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database" <doi:10.1093/nar/gkz896>) to display field-specific miRNA-mRNA interactions. The most important functions are available as a Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
Maintained by Julian Friedrich. Last updated 3 years ago.
63.5 match 2.19 score 31 scriptsbioc
IntramiRExploreR:Predicting Targets for Drosophila Intragenic miRNAs
Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR.
Maintained by Surajit Bhattacharya. Last updated 5 months ago.
softwaremicroarraygenetargetstatisticalmethodgeneexpressiongeneprediction
23.0 match 4.60 score 4 scriptsbioc
miRNApath:miRNApath: Pathway Enrichment for miRNA Expression Data
This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes.
Maintained by James M. Ward. Last updated 5 months ago.
annotationpathwaysdifferentialexpressionnetworkenrichmentmirna
23.2 match 4.30 score 3 scriptsbioc
miRNAmeConverter:Convert miRNA Names to Different miRBase Versions
Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/).
Maintained by Stefan J. Haunsberger. Last updated 5 months ago.
26.3 match 3.78 score 4 scripts
w123yu
sRNAGenetic:Analysis of sRNA Expression Changes During Plant Polyploidization
The most important function of the R package sRNAGenetic is the genetic effects analysis of miRNA after plant polyploidization via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of ELD (expression level domainance) by comparing the total expression of the miRNA in allotetraploids with the expression level in the parent species.
Maintained by Yu qing Wu. Last updated 3 years ago.
32.0 match 1 stars 2.70 score 1 scriptsbioc
TCGAbiolinks:TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data
The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines.
Maintained by Tiago Chedraoui Silva. Last updated 25 days ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksequencingsurvivalsoftwarebiocbioconductorgdcintegrative-analysistcgatcga-datatcgabiolinks
6.0 match 305 stars 14.45 score 1.6k scripts 6 dependents
hanjunwei-lab
MiRSEA:'MicroRNA' Set Enrichment Analysis
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Maintained by Junwei Han. Last updated 5 years ago.
statisticspathwaysmicrornaenrichment analysis
17.2 match 4.51 score 16 scripts
moosa-r
rbioapi:User-Friendly R Interface to Biologic Web Services' API
Currently fully supports Enrichr, JASPAR, miEAA, PANTHER, Reactome, STRING, and UniProt! The goal of rbioapi is to provide a user-friendly and consistent interface to biological databases and services. In a way that insulates the user from the technicalities of using web services API and creates a unified and easy-to-use interface to biological and medical web services. This is an ongoing project; New databases and services will be added periodically. Feel free to suggest any databases or services you often use.
Maintained by Moosa Rezwani. Last updated 1 months ago.
api-clientbioinformaticsbiologyenrichmentenrichment-analysisenrichrjasparmieaaover-representation-analysispantherreactomestringuniprot
10.1 match 20 stars 7.60 score 55 scriptsbioc
miRNAtap:miRNAtap: microRNA Targets - Aggregated Predictions
The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation).
Maintained by T. Ian Simpson. Last updated 5 months ago.
softwareclassificationmicroarraysequencingmirna
13.5 match 4.94 score 44 scriptsbioc
primirTSS:Prediction of pri-miRNA Transcription Start Site
A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.
Maintained by Pumin Li. Last updated 5 months ago.
immunooncologysequencingrnaseqgeneticspreprocessingtranscriptiongeneregulation
14.7 match 4.48 score 2 scriptsbioc
NoRCE:NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
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.
Maintained by Gulden Olgun. Last updated 5 months ago.
biologicalquestiondifferentialexpressiongenomeannotationgenesetenrichmentgenetargetgenomeassemblygo
14.1 match 1 stars 4.60 score 6 scripts
mcanouil
NACHO:NanoString Quality Control Dashboard
NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.
Maintained by Mickaël Canouil. Last updated 1 years ago.
mirnamrnananostringnormalisationquality-controlshiny
11.8 match 8 stars 5.41 score 32 scriptsbioc
scanMiRApp:scanMiR shiny application
A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs.
Maintained by Pierre-Luc Germain. Last updated 5 months ago.
mirnasequencematchingguishinyapps
13.0 match 4.88 score 19 scriptsbioc
rcellminer:rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines
The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data.
Maintained by Augustin Luna. Last updated 5 months ago.
acghcellbasedassayscopynumbervariationgeneexpressionpharmacogenomicspharmacogeneticsmirnacheminformaticsvisualizationsoftwaresystemsbiology
11.0 match 5.71 score 113 scriptsbioc
discordant:The Discordant Method: A Novel Approach for Differential Correlation
Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016).
Maintained by McGrath Max. Last updated 5 months ago.
immunooncologybiologicalquestionstatisticalmethodmrnamicroarraymicroarraygeneticsrnaseqcpp
10.4 match 10 stars 6.05 score 14 scriptsbioc
gINTomics:Multi-Omics data integration
gINTomics is an R package for Multi-Omics data integration and visualization. gINTomics is designed to detect the association between the expression of a target and of its regulators, taking into account also their genomics modifications such as Copy Number Variations (CNV) and methylation. What is more, gINTomics allows integration results visualization via a Shiny-based interactive app.
Maintained by Angelo Velle. Last updated 5 months ago.
geneexpressionrnaseqmicroarrayvisualizationcopynumbervariationgenetargetquarto
12.2 match 3 stars 5.08 score 3 scriptsbioc
globalSeq:Global Test for Counts
The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data.
Maintained by Armin Rauschenberger. Last updated 5 months ago.
geneexpressionexonarraydifferentialexpressiongenomewideassociationtranscriptomicsdimensionreductionregressionsequencingwholegenomernaseqexomeseqmirnamultiplecomparison
11.0 match 1 stars 5.32 score 4 scriptsbioc
multiMiR:Integration of multiple microRNA-target databases with their disease and drug associations
A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR).
Maintained by Spencer Mahaffey. Last updated 5 months ago.
mirnadatahomo_sapiens_datamus_musculus_datarattus_norvegicus_dataorganismdatamicrorna-sequencesql
6.7 match 20 stars 8.45 score 141 scripts
tacazares
SeedMatchR:Find Matches to Canonical SiRNA Seeds in Genomic Features
On-target gene knockdown using siRNA ideally results from binding fully complementary regions in mRNA transcripts to induce cleavage. Off-target siRNA gene knockdown can occur through several modes, one being a seed-mediated mechanism mimicking miRNA gene regulation. Seed-mediated off-target effects occur when the ~8 nucleotides at the 5’ end of the guide strand, called a seed region, bind the 3’ untranslated regions of mRNA, causing reduced translation. Experiments using siRNA knockdown paired with RNA-seq can be used to detect siRNA sequences with potential off-target effects driven by the seed region. 'SeedMatchR' provides tools for exploring and detecting potential seed-mediated off-target effects of siRNA in RNA-seq experiments. 'SeedMatchR' is designed to extend current differential expression analysis tools, such as 'DESeq2', by annotating results with predicted seed matches. Using publicly available data, we demonstrate the ability of 'SeedMatchR' to detect cumulative changes in differential gene expression attributed to siRNA seed regions.
Maintained by Tareian Cazares. Last updated 1 years ago.
deseq2-analysismirnarna-seqsirnatranscriptomics
11.5 match 7 stars 4.54 score 7 scriptsbioc
TargetScore:TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information
Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.
Maintained by Yue Li. Last updated 5 months ago.
11.5 match 4.00 score 9 scriptsbioc
MiRaGE:MiRNA Ranking by Gene Expression
The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile.
Maintained by Y-h. Taguchi. Last updated 5 months ago.
immunooncologymicroarraygeneexpressionrnaseqsequencingsage
10.9 match 3.85 score 35 scriptsbioc
QuasR:Quantify and Annotate Short Reads in R
This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. Read alignments are either generated through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments) or Rhisat2 (data from RNA-seq experiments that require spliced alignments), or can be provided in the form of bam files.
Maintained by Michael Stadler. Last updated 22 days ago.
geneticspreprocessingsequencingchipseqrnaseqmethylseqcoveragealignmentqualitycontrolimmunooncologycurlbzip2xz-utilszlibcpp
4.7 match 6 stars 8.70 score 79 scripts 1 dependentsbioc
GIGSEA:Genotype Imputed Gene Set Enrichment Analysis
We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.
Maintained by Shijia Zhu. Last updated 5 months ago.
genesetenrichmentsnpvariantannotationgeneexpressiongeneregulationregressiondifferentialexpression
9.0 match 4.30 score 2 scripts
pbiecek
bgmm:Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling
Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.
Maintained by Przemyslaw Biecek. Last updated 2 years ago.
8.6 match 2 stars 4.22 score 55 scripts 1 dependents
lightbluetitan
OncoDataSets:A Comprehensive Collection of Cancer Types and Cancer-related DataSets
Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.
Maintained by Renzo Caceres Rossi. Last updated 3 months ago.
7.2 match 3 stars 4.18 score 6 scriptsbioc
CHRONOS:CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis
A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs.
Maintained by Panos Balomenos. Last updated 5 months ago.
systemsbiologygraphandnetworkpathwayskeggopenjdk
7.1 match 3.86 score 12 scripts
kechrislab
SmCCNet:Sparse Multiple Canonical Correlation Network Analysis Tool
A canonical correlation based framework (SmCCNet) designed for the construction of phenotype-specific multi-omics networks. This framework adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. It offers a streamlined setup process that can be tailored manually or configured automatically, ensuring a flexible and user-friendly experience.
Maintained by Weixuan Liu. Last updated 11 months ago.
3.8 match 28 stars 6.40 score 30 scriptsbioc
mirIntegrator:Integrating microRNA expression into signaling pathways for pathway analysis
Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels.
Maintained by Diana Diaz. Last updated 5 months ago.
networkmicroarraygraphandnetworkpathwayskegg
7.3 match 1 stars 3.30 score 2 scriptscran
miRNAss:Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples
Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data.
Maintained by Cristian Yones. Last updated 4 years ago.
8.7 match 1 stars 2.70 score 3 scriptshanjunwei-lab
IDMIR:Identification of Dysregulated MiRNAs Based on MiRNA-MiRNA Interaction Network
A systematic biology tool was developed to identify dysregulated miRNAs via a miRNA-miRNA interaction network. 'IDMIR' first constructed a weighted miRNA interaction network through integrating miRNA-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific-disease context, and then, it used a network propagation algorithm on the network to identify significantly dysregulated miRNAs.
Maintained by Junwei Han. Last updated 1 years ago.
11.4 match 2.00 score 2 scriptsbioc
ExiMiR:R functions for the normalization of Exiqon miRNA array data
This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported.
Maintained by Sylvain Gubian. Last updated 5 months ago.
microarrayonechanneltwochannelpreprocessinggeneexpressiontranscription
7.9 match 2.78 score 3 scriptsbioc
affycoretools:Functions useful for those doing repetitive analyses with Affymetrix GeneChips
Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see.
Maintained by James W. MacDonald. Last updated 5 months ago.
reportwritingmicroarrayonechannelgeneexpression
3.5 match 6.07 score 117 scriptsbioc
miRcomp:Tools to assess and compare miRNA expression estimatation methods
Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves.
Maintained by Matthew N. McCall. Last updated 5 months ago.
softwareqpcrpreprocessingqualitycontrol
6.2 match 3.30 score 1 scriptsbioc
circRNAprofiler:circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs
R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.
Maintained by Simona Aufiero. Last updated 5 months ago.
annotationstructuralpredictionfunctionalpredictiongenepredictiongenomeassemblydifferentialexpression
3.0 match 10 stars 5.78 score 5 scriptsbioc
RTCGAToolbox:A new tool for exporting TCGA Firehose data
Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis.
Maintained by Marcel Ramos. Last updated 3 months ago.
differentialexpressiongeneexpressionsequencing
1.7 match 18 stars 9.75 score 76 scripts 5 dependentsbioc
BUS:Gene network reconstruction
This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical).
Maintained by Yuanhua Liu. Last updated 5 months ago.
3.6 match 3.90 score 6 scriptsbioc
systemPipeR:systemPipeR: Workflow Environment for Data Analysis and Report Generation
systemPipeR is a multipurpose data analysis workflow environment that unifies R with command-line tools. It enables scientists to analyze many types of large- or small-scale data on local or distributed computer systems with a high level of reproducibility, scalability and portability. At its core is a command-line interface (CLI) that adopts the Common Workflow Language (CWL). This design allows users to choose for each analysis step the optimal R or command-line software. It supports both end-to-end and partial execution of workflows with built-in restart functionalities. Efficient management of complex analysis tasks is accomplished by a flexible workflow control container class. Handling of large numbers of input samples and experimental designs is facilitated by consistent sample annotation mechanisms. As a multi-purpose workflow toolkit, systemPipeR enables users to run existing workflows, customize them or design entirely new ones while taking advantage of widely adopted data structures within the Bioconductor ecosystem. Another important core functionality is the generation of reproducible scientific analysis and technical reports. For result interpretation, systemPipeR offers a wide range of plotting functionality, while an associated Shiny App offers many useful functionalities for interactive result exploration. The vignettes linked from this page include (1) a general introduction, (2) a description of technical details, and (3) a collection of workflow templates.
Maintained by Thomas Girke. Last updated 5 months ago.
geneticsinfrastructuredataimportsequencingrnaseqriboseqchipseqmethylseqsnpgeneexpressioncoveragegenesetenrichmentalignmentqualitycontrolimmunooncologyreportwritingworkflowstepworkflowmanagement
1.2 match 53 stars 11.56 score 344 scripts 3 dependentsbioc
padma:Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis
Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles.
Maintained by Andrea Rau. Last updated 5 months ago.
softwarestatisticalmethodprincipalcomponentgeneexpressionpathwaysrnaseqbiocartamethylseq
2.5 match 3 stars 4.95 score 2 scriptsbioc
MiChip:MiChip Parsing and Summarizing Functions
This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages.
Maintained by Jonathon Blake. Last updated 5 months ago.
3.6 match 3.30 score 1 scripts
stephanartmann
miRtest:Combined miRNA- And mRNA-Testing
Package for combined miRNA- and mRNA-testing.
Maintained by Stephan Artmann. Last updated 1 years ago.
5.4 match 1 stars 2.08 score 12 scriptsbioc
EpiMix:EpiMix: an integrative tool for the population-level analysis of DNA methylation
EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs.
Maintained by Yuanning Zheng. Last updated 5 months ago.
softwareepigeneticspreprocessingdnamethylationgeneexpressiondifferentialmethylation
2.5 match 1 stars 4.48 score 7 scripts 1 dependents
openbiox
UCSCXenaShiny:Interactive Analysis of UCSC Xena Data
Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Maintained by Shixiang Wang. Last updated 4 months ago.
cancer-datasetshiny-appsucsc-xena
1.2 match 96 stars 8.54 score 35 scriptsbioc
BioCor:Functional similarities
Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships...
Maintained by Lluís Revilla Sancho. Last updated 5 months ago.
statisticalmethodclusteringgeneexpressionnetworkpathwaysnetworkenrichmentsystemsbiologybioconductor-packagesbioinformaticsfunctional-similaritygenegene-setspathway-analysissimilaritysimilarity-measurement
1.5 match 14 stars 6.59 scorebioc
STATegRa:Classes and methods for multi-omics data integration
Classes and tools for multi-omics data integration.
Maintained by David Gomez-Cabrero. Last updated 5 months ago.
softwarestatisticalmethodclusteringdimensionreductionprincipalcomponent
2.3 match 4.15 score 3 scriptscran
CeRNASeek:Identification and Analysis of ceRNA Regulation
Provides several functions to identify and analyse miRNA sponge, including popular methods for identifying miRNA sponge interactions, two types of global ceRNA regulation prediction methods and four types of context-specific prediction methods( Li Y et al.(2017) <doi:10.1093/bib/bbx137>), which are based on miRNA-messenger RNA regulation alone, or by integrating heterogeneous data, respectively. In addition, For predictive ceRNA relationship pairs, this package provides several downstream analysis algorithms, including regulatory network analysis and functional annotation analysis, as well as survival prognosis analysis based on expression of ceRNA ternary pair.
Maintained by Mengying Zhang. Last updated 5 years ago.
competing endogenous rna (cerna)geneexpressiontripletfunctionsoftware
9.4 match 1.00 score
fischuu
hoardeR:Collect and Retrieve Annotation Data for Various Genomic Data Using Different Webservices
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
Maintained by Daniel Fischer. Last updated 11 months ago.
2.3 match 1 stars 3.70 score 6 scriptsbioc
srnadiff:Finding differentially expressed unannotated genomic regions from RNA-seq data
srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values.
Maintained by Zytnicki Matthias. Last updated 2 months ago.
immunooncologygeneexpressioncoveragesmallrnaepigeneticsstatisticalmethodpreprocessingdifferentialexpressioncpp
2.3 match 3.70 score 3 scriptsbioc
coMET:coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns
Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as lon:g as the data can be translated to genomic level and for any species.
Maintained by Tiphaine Martin. Last updated 15 hours ago.
softwaredifferentialmethylationvisualizationsequencinggeneticsfunctionalgenomicsmicroarraymethylationarraymethylseqchipseqdnaseqriboseqrnaseqexomeseqdnamethylationgenomewideassociationmotifannotation
1.8 match 4.41 score 17 scripts
regisoc
kibior:A Simple Data Management and Sharing Tool
An interface to store, retrieve, search, join and share datasets, based on Elasticsearch (ES) API. As a decentralized, FAIR and collaborative search engine and database effort, it proposes a simple push/pull/search mechanism only based on ES, a tool which can be deployed on nearly any hardware. It is a high-level R-ES binding to ease data usage using 'elastic' package (S. Chamberlain (2020)) <https://docs.ropensci.org/elastic/>, extends joins from 'dplyr' package (H. Wickham et al. (2020)) <https://dplyr.tidyverse.org/> and integrates specific biological format importation with Bioconductor packages such as 'rtracklayer' (M. Lawrence and al. (2009) <doi:10.1093/bioinformatics/btp328>) <http://bioconductor.org/packages/rtracklayer>, 'Biostrings' (H. Pagès and al. (2020) <doi:10.18129/B9.bioc.Biostrings>) <http://bioconductor.org/packages/Biostrings>, and 'Rsamtools' (M. Morgan and al. (2020) <doi:10.18129/B9.bioc.Rsamtools>) <http://bioconductor.org/packages/Rsamtools>, but also a long list of more common ones with 'rio' (C-h. Chan and al. (2018)) <https://cran.r-project.org/package=rio>.
Maintained by Régis Ongaro-Carcy. Last updated 4 years ago.
dataimportdatarepresentationthirdpartyclientdata-sciencedatabasedatasetselasticsearchelasticsearch-clientpush-pullsearchsearch-engine
1.1 match 3 stars 4.48 score 8 scriptsbioc
trackViewer:A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data
Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data.
Maintained by Jianhong Ou. Last updated 2 months ago.
0.5 match 8.71 score 145 scripts 2 dependentsbioc
tRanslatome:Comparison between multiple levels of gene expression
Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots.
Maintained by Toma Tebaldi. Last updated 5 months ago.
cellbiologygeneregulationregulationgeneexpressiondifferentialexpressionmicroarrayhighthroughputsequencingqualitycontrolgomultiplecomparisonsbioinformatics
0.5 match 3.30 score 2 scripts
boulesteix
ipflasso:Integrative Lasso with Penalty Factors
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.
Maintained by Anne-Laure Boulesteix. Last updated 5 years ago.
0.5 match 1 stars 2.01 score 34 scripts