Showing 156 of total 156 results (show query)
luckinet
ontologics:Code-Logics to Handle Ontologies
Provides tools to build and work with an ontology of linked (open) data in a tidy workflow. It is inspired by the Food and Agrilculture Organizations (FAO) caliper platform <https://www.fao.org/statistics/caliper/web/> and makes use of the Simple Knowledge Organisation System (SKOS).
Maintained by Steffen Ehrmann. Last updated 2 months ago.
103.3 match 3 stars 6.39 score 17 scripts 1 dependents
bioc
ontoProc:processing of ontologies of anatomy, cell lines, and so on
Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.
Maintained by Vincent Carey. Last updated 3 days ago.
infrastructuregobioinformaticsgenomicsontology
54.3 match 3 stars 6.37 score 75 scripts 2 dependentsbioc
OmnipathR:OmniPath web service client and more
A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github).
Maintained by Denes Turei. Last updated 18 days ago.
graphandnetworknetworkpathwayssoftwarethirdpartyclientdataimportdatarepresentationgenesignalinggeneregulationsystemsbiologytranscriptomicssinglecellannotationkeggcomplexesenzyme-ptmnetworksnetworks-biologyomnipathproteinsquarto
27.7 match 126 stars 9.90 score 226 scripts 2 dependentsbioc
DOSE:Disease Ontology Semantic and Enrichment analysis
This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data.
Maintained by Guangchuang Yu. Last updated 5 months ago.
annotationvisualizationmultiplecomparisongenesetenrichmentpathwayssoftwaredisease-ontologyenrichment-analysissemantic-similarity
13.4 match 119 stars 14.97 score 2.0k scripts 61 dependents
eltebioinformatics
mulea:Enrichment Analysis Using Multiple Ontologies and False Discovery Rate
Background - Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. Results - mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies. mulea expands beyond traditional tools by incorporating a wide range of ontologies, encompassing Gene Ontology, pathways, regulatory elements, genomic locations, and protein domains. This flexibility enables researchers to tailor enrichment analysis to their specific questions, such as identifying enriched transcriptional regulators in gene expression data or overrepresented protein domains in protein sets. To facilitate seamless analysis, mulea provides gene sets (in standardised GMT format) for 27 model organisms, covering 22 ontology types from 16 databases and various identifiers resulting in almost 900 files. Additionally, the muleaData ExperimentData Bioconductor package simplifies access to these pre-defined ontologies. Finally, mulea's architecture allows for easy integration of user-defined ontologies, or GMT files from external sources (e.g., MSigDB or Enrichr), expanding its applicability across diverse research areas. Conclusions - mulea is distributed as a CRAN R package. It offers researchers a powerful and flexible toolkit for functional enrichment analysis, addressing limitations of traditional tools with its progressive eFDR and by supporting a variety of ontologies. Overall, mulea fosters the exploration of diverse biological questions across various model organisms.
Maintained by Tamas Stirling. Last updated 3 months ago.
annotationdifferentialexpressiongeneexpressiongenesetenrichmentgographandnetworkmultiplecomparisonpathwaysreactomesoftwaretranscriptionvisualizationenrichmentenrichment-analysisfunctional-enrichment-analysisgene-set-enrichmentontologiestranscriptomicscpp
25.7 match 28 stars 7.36 score 34 scriptsbioc
rols:An R interface to the Ontology Lookup Service
The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R.
Maintained by Laurent Gatto. Last updated 5 months ago.
immunooncologysoftwareannotationmassspectrometrygo
19.8 match 11 stars 8.30 score 89 scripts 5 dependents
sergeitarasov
ontoFAST:Interactive Annotation of Characters with Biological Ontologies
Tools for annotating characters (character matrices) with anatomical and phenotype ontologies. Includes functions for visualising character annotations and creating simple queries using ontological relationships.
Maintained by Sergei Tarasov. Last updated 3 years ago.
annotationscharacter-matricescharactersontologyphylogenetics
51.7 match 2 stars 3.00 score 5 scripts
papatheodorou-group
scOntoMatch:Aligning Ontology Annotation Across Single Cell Datasets with 'scOntoMatch'
Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.
Maintained by Yuyao Song. Last updated 1 years ago.
33.0 match 7 stars 4.54 score 6 scriptsbioc
GOexpress:Visualise microarray and RNAseq data using gene ontology annotations
The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.
Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.
softwaregeneexpressiontranscriptiondifferentialexpressiongenesetenrichmentdatarepresentationclusteringtimecoursemicroarraysequencingrnaseqannotationmultiplecomparisonpathwaysgovisualizationimmunooncologybioconductorbioconductor-packagebioconductor-statsgeneontologygeneset-enrichment
20.6 match 9 stars 6.75 score 31 scriptsbioc
rWikiPathways:rWikiPathways - R client library for the WikiPathways API
Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions.
Maintained by Egon Willighagen. Last updated 5 months ago.
visualizationgraphandnetworkthirdpartyclientnetworkmetabolomicsbioinformaticsdata-accesspathways
14.2 match 15 stars 9.23 score 131 scripts 3 dependentsbioc
topGO:Enrichment Analysis for Gene Ontology
topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.
Maintained by Adrian Alexa. Last updated 5 months ago.
12.8 match 8.96 score 2.0k scripts 20 dependentsbioc
GOSemSim:GO-terms Semantic Similarity Measures
The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively.
Maintained by Guangchuang Yu. Last updated 5 months ago.
annotationgoclusteringpathwaysnetworksoftwarebioinformaticsgene-ontologysemantic-similaritycpp
8.0 match 63 stars 14.12 score 708 scripts 68 dependentsbioc
wppi:Weighting protein-protein interactions
Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.
Maintained by Ana Galhoz. Last updated 5 months ago.
graphandnetworknetworkpathwayssoftwaregenesignalinggenetargetsystemsbiologytranscriptomicsannotationgene-ontologygene-prioritizationhuman-phenotype-ontologyomnipathppi-networksrandom-walk-with-restartquarto
26.0 match 1 stars 4.30 score 4 scriptsbioc
fobitools:Tools for Manipulating the FOBI Ontology
A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data.
Maintained by Pol Castellano-Escuder. Last updated 4 months ago.
massspectrometrymetabolomicssoftwarevisualizationbiomedicalinformaticsgraphandnetworkannotationcheminformaticspathwaysgenesetenrichmentbiological-intrerpretationbiological-knowledgebiological-significance-analysisenrichment-analysisfood-biomarker-ontologyknowledge-graphnutritionobofoundryontologytext-mining
20.3 match 1 stars 5.08 score 5 scripts
bernd-mueller
epos:Epilepsy Ontologies' Similarities
Analysis and visualization of similarities between epilepsy ontologies based on text mining results by comparing ranked lists of co-occurring drug terms in the BioASQ corpus. The ranked result lists of neurological drug terms co-occurring with terms from the epilepsy ontologies EpSO, ESSO, EPILONT, EPISEM and FENICS undergo further analysis. The source data to create the ranked lists of drug names is produced using the text mining workflows described in Mueller, Bernd and Hagelstein, Alexandra (2016) <doi:10.4126/FRL01-006408558>, Mueller, Bernd et al. (2017) <doi:10.1007/978-3-319-58694-6_22>, Mueller, Bernd and Rebholz-Schuhmann, Dietrich (2020) <doi:10.1007/978-3-030-43887-6_52>, and Mueller, Bernd et al. (2022) <doi:10.1186/s13326-021-00258-w>.
Maintained by Bernd Mueller. Last updated 1 years ago.
25.2 match 4.03 score 53 scriptsbioc
annotate:Annotation for microarrays
Using R enviroments for annotation.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
8.3 match 11.41 score 812 scripts 243 dependents
theme-ontology
stoRy:Download, Explore, and Analyze Literary Theme Ontology Data
Download, explore, and analyze Literary Theme Ontology themes and thematically annotated story data. To learn more about the project visit <https://github.com/theme-ontology/theming> and <https://www.themeontology.org>.
Maintained by Paul Sheridan. Last updated 2 years ago.
20.4 match 7 stars 4.59 score 11 scripts
daniel-jg
ontologyIndex:Reading Ontologies into R
Functions for reading ontologies into R as lists and manipulating sets of ontological terms - 'ontologyX: A suite of R packages for working with ontological data', Greene et al 2017 <doi:10.1093/bioinformatics/btw763>.
Maintained by Daniel Greene. Last updated 1 years ago.
13.0 match 6 stars 7.15 score 286 scripts 27 dependentsbioc
orthogene:Interspecies gene mapping
`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.
Maintained by Brian Schilder. Last updated 5 months ago.
geneticscomparativegenomicspreprocessingphylogeneticstranscriptomicsgeneexpressionanimal-modelsbioconductorbioconductor-packagebioinformaticsbiomedicinecomparative-genomicsevolutionary-biologygenesgenomicsontologiestranslational-research
10.0 match 42 stars 7.85 score 31 scripts 2 dependents
jpquast
protti:Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools
Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.
Maintained by Jan-Philipp Quast. Last updated 5 months ago.
data-analysislip-msmass-spectrometryomicsproteinproteomicssystems-biology
8.9 match 61 stars 8.58 score 83 scriptsbioc
goSorensen:Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO)
This package implements inferential methods to compare gene lists in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items.
Maintained by Pablo Flores. Last updated 5 months ago.
annotationgogenesetenrichmentsoftwaremicroarraypathwaysgeneexpressionmultiplecomparisongraphandnetworkreactomeclusteringkegg
16.8 match 4.56 score 12 scriptsbioc
BiocGenerics:S4 generic functions used in Bioconductor
The package defines many S4 generic functions used in Bioconductor.
Maintained by Hervé Pagès. Last updated 1 months ago.
infrastructurebioconductor-packagecore-package
5.0 match 12 stars 14.22 score 612 scripts 2.2k dependentsdaniel-jg
ontologySimilarity:Calculating Ontological Similarities
Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Maintained by Daniel Greene. Last updated 12 months ago.
16.4 match 4.09 score 68 scripts 1 dependents
overton-group
eHDPrep:Quality Control and Semantic Enrichment of Datasets
A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.
Maintained by Ian Overton. Last updated 2 years ago.
data-qualityhealth-informaticssemantic-enrichment
13.5 match 8 stars 4.90 score 10 scriptsbioc
OmicsMLRepoR:Search harmonized metadata created under the OmicsMLRepo project
This package provides functions to browse the harmonized metadata for large omics databases. This package also supports data navigation if the metadata incorporates ontology.
Maintained by Sehyun Oh. Last updated 1 months ago.
softwareinfrastructuredatarepresentation
11.7 match 5.40 score 14 scriptsdaniel-jg
ontologyPlot:Visualising Sets of Ontological Terms
Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.
Maintained by Daniel Greene. Last updated 1 years ago.
14.0 match 4.48 score 50 scripts 5 dependents
ramiromagno
gwasrapidd:'REST' 'API' Client for the 'NHGRI'-'EBI' 'GWAS' Catalog
'GWAS' R 'API' Data Download. This package provides easy access to the 'NHGRI'-'EBI' 'GWAS' Catalog data by accessing the 'REST' 'API' <https://www.ebi.ac.uk/gwas/rest/docs/api/>.
Maintained by Ramiro Magno. Last updated 1 years ago.
thirdpartyclientbiomedicalinformaticsgenomewideassociationsnpassociation-studiesgwas-cataloghumanrest-clienttraittrait-ontology
7.5 match 95 stars 8.10 score 49 scripts 1 dependentsbioc
struct:Statistics in R Using Class-based Templates
Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates.
Maintained by Gavin Rhys Lloyd. Last updated 5 months ago.
9.6 match 6.04 score 76 scripts 3 dependentsbioc
fgga:Hierarchical ensemble method based on factor graph
Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.
Maintained by Flavio Spetale. Last updated 5 months ago.
softwarestatisticalmethodclassificationnetworknetworkinferencesupportvectormachinegraphandnetworkgo
12.1 match 3 stars 4.48 score 6 scriptsbioc
goProfiles:goProfiles: an R package for the statistical analysis of functional profiles
The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'.
Maintained by Alex Sanchez. Last updated 5 months ago.
annotationgogeneexpressiongenesetenrichmentgraphandnetworkmicroarraymultiplecomparisonpathwayssoftware
9.8 match 5.48 score 6 scripts 1 dependents
ctn-0094
DOPE:Drug Ontology Parsing Engine
Provides information on drug names (brand, generic and street) for drugs tracked by the DEA. There are functions that will search synonyms and return the drug names and types. The vignettes have extensive information on the work done to create the data for the package.
Maintained by Raymond Balise. Last updated 4 years ago.
6.5 match 21 stars 7.83 score 31 scriptsbioc
goseq:Gene Ontology analyser for RNA-seq and other length biased data
Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data.
Maintained by Federico Marini. Last updated 5 months ago.
immunooncologysequencinggogeneexpressiontranscriptionrnaseqdifferentialexpressionannotationgenesetenrichmentkeggpathwayssoftware
5.2 match 1 stars 9.67 score 636 scripts 9 dependentsbioc
simona:Semantic Similarity on Bio-Ontologies
This package implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. It provides a robust toolbox supporting over 70 methods for semantic similarity analysis.
Maintained by Zuguang Gu. Last updated 5 months ago.
softwareannotationgobiomedicalinformaticscpp
7.5 match 16 stars 6.59 score 27 scripts 1 dependentsbioc
GSEABase:Gene set enrichment data structures and methods
This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA).
Maintained by Bioconductor Package Maintainer. Last updated 1 months ago.
geneexpressiongenesetenrichmentgraphandnetworkgokegg
4.8 match 10.27 score 1.5k scripts 77 dependentsbioc
limma:Linear Models for Microarray and Omics Data
Data analysis, linear models and differential expression for omics data.
Maintained by Gordon Smyth. Last updated 5 days ago.
exonarraygeneexpressiontranscriptionalternativesplicingdifferentialexpressiondifferentialsplicinggenesetenrichmentdataimportbayesianclusteringregressiontimecoursemicroarraymicrornaarraymrnamicroarrayonechannelproprietaryplatformstwochannelsequencingrnaseqbatcheffectmultiplecomparisonnormalizationpreprocessingqualitycontrolbiomedicalinformaticscellbiologycheminformaticsepigeneticsfunctionalgenomicsgeneticsimmunooncologymetabolomicsproteomicssystemsbiologytranscriptomics
3.5 match 13.81 score 16k scripts 585 dependentsbioc
Category:Category Analysis
A collection of tools for performing category (gene set enrichment) analysis.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
annotationgopathwaysgenesetenrichment
6.0 match 7.93 score 183 scripts 16 dependentsbioc
SGCP:SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks
SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
Maintained by Niloofar AghaieAbiane. Last updated 5 months ago.
geneexpressiongenesetenrichmentnetworkenrichmentsystemsbiologyclassificationclusteringdimensionreductiongraphandnetworkneuralnetworknetworkmrnamicroarrayrnaseqvisualizationbioinformaticsgenecoexpressionnetworkgraphsnetworkclusteringnetworksself-trainingsemi-supervised-learningunsupervised-learning
8.9 match 2 stars 5.12 score 44 scriptsbioc
AnnotationDbi:Manipulation of SQLite-based annotations in Bioconductor
Implements a user-friendly interface for querying SQLite-based annotation data packages.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
annotationmicroarraysequencinggenomeannotationbioconductor-packagecore-package
3.0 match 9 stars 15.05 score 3.6k scripts 769 dependentsbioc
xCell2:A Tool for Generic Cell Type Enrichment Analysis
xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.
Maintained by Almog Angel. Last updated 2 months ago.
geneexpressiontranscriptomicsmicroarrayrnaseqsinglecelldifferentialexpressionimmunooncologygenesetenrichment
7.2 match 6 stars 6.17 score 15 scriptsluckinet
arealDB:Harmonise and Integrate Heterogeneous Areal Data
Many relevant applications in the environmental and socioeconomic sciences use areal data, such as biodiversity checklists, agricultural statistics, or socioeconomic surveys. For applications that surpass the spatial, temporal or thematic scope of any single data source, data must be integrated from several heterogeneous sources. Inconsistent concepts, definitions, or messy data tables make this a tedious and error-prone process. 'arealDB' tackles those problems and helps the user to integrate a harmonised databases of areal data. Read the paper at Ehrmann, Seppelt & Meyer (2020) <doi:10.1016/j.envsoft.2020.104799>.
Maintained by Steffen Ehrmann. Last updated 1 months ago.
8.0 match 2 stars 5.41 score 15 scriptsbioc
ViSEAGO:ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity
The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility.
Maintained by Aurelien Brionne. Last updated 2 months ago.
softwareannotationgogenesetenrichmentmultiplecomparisonclusteringvisualization
6.3 match 6.64 score 22 scripts
ropensci
biomartr:Genomic Data Retrieval
Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve functional annotation for genomic loci. In addition, users can download entire databases such as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr', 'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.
Maintained by Hajk-Georg Drost. Last updated 1 months ago.
biomartgenomic-data-retrievalannotation-retrievaldatabase-retrievalncbiensemblbiological-data-retrievalensembl-serversgenomegenome-annotationgenome-retrievalgenomicsmeta-analysismetagenomicsncbi-genbankpeer-reviewedproteomesequenced-genomes
3.5 match 218 stars 11.35 score 129 scripts 3 dependentsbioc
BioNAR:Biological Network Analysis in R
the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.
Maintained by Anatoly Sorokin. Last updated 18 days ago.
softwaregraphandnetworknetwork
6.5 match 3 stars 5.90 score 35 scriptsbioc
missMethyl:Analysing Illumina HumanMethylation BeadChip Data
Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes.
Maintained by Belinda Phipson. Last updated 13 days ago.
normalizationdnamethylationmethylationarraygenomicvariationgeneticvariabilitydifferentialmethylationgenesetenrichment
5.2 match 7.24 score 300 scripts 1 dependents
interstellar-consultation-services
covid19dbcand:Selected 'Drugbank' Drugs for COVID-19 Treatment Related Data in R Format
Provides different datasets parsed from 'Drugbank' <https://www.drugbank.ca/covid-19> database using 'dbparser' package. It is a smaller version from 'dbdataset' package. It contains only information about COVID-19 possible treatment.
Maintained by Mohammed Ali. Last updated 11 months ago.
datasetdbparserdrugbankdrugbank-database
8.4 match 3 stars 4.48 score 6 scriptsbioc
structToolbox:Data processing & analysis tools for Metabolomics and other omics
An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs.
Maintained by Gavin Rhys Lloyd. Last updated 25 days ago.
workflowstepmetabolomicsbioconductor-packagedimslc-msmachine-learningmultivariate-analysisstatisticsunivariate
5.9 match 10 stars 6.26 score 12 scriptsbioc
ClusterJudge:Judging Quality of Clustering Methods using Mutual Information
ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview.
Maintained by Adrian Pasculescu. Last updated 2 months ago.
softwarestatisticalmethodclusteringgeneexpressiongo
10.3 match 3.48 score 3 scriptsbioc
rGREAT:GREAT Analysis - Functional Enrichment on Genomic Regions
GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions.
Maintained by Zuguang Gu. Last updated 3 days ago.
genesetenrichmentgopathwayssoftwaresequencingwholegenomegenomeannotationcoveragecpp
3.6 match 86 stars 9.96 score 320 scripts 1 dependentsbioc
pogos:PharmacOGenomics Ontology Support
Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies.
Maintained by VJ Carey. Last updated 2 months ago.
pharmacogenomicspooledscreensimmunooncology
8.2 match 4.30 score 10 scriptsbioc
EnrichDO:a Global Weighted Model for Disease Ontology Enrichment Analysis
To implement disease ontology (DO) enrichment analysis, this package is designed and presents a double weighted model based on the latest annotations of the human genome with DO terms, by integrating the DO graph topology on a global scale. This package exhibits high accuracy that it can identify more specific DO terms, which alleviates the over enriched problem. The package includes various statistical models and visualization schemes for discovering the associations between genes and diseases from biological big data.
Maintained by Hongyu Fu. Last updated 4 months ago.
annotationvisualizationgenesetenrichmentsoftware
7.4 match 4.74 score 9 scripts
nanxstats
protr:Generating Various Numerical Representation Schemes for Protein Sequences
Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042>. For full functionality, the software 'ncbi-blast+' is needed, see <https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html> for more information.
Maintained by Nan Xiao. Last updated 6 months ago.
bioinformaticsfeature-engineeringfeature-extractionmachine-learningpeptidesprotein-sequencessequence-analysis
3.4 match 52 stars 10.02 score 173 scripts 3 dependentsbioc
ASURAT:Functional annotation-driven unsupervised clustering for single-cell data
ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
Maintained by Keita Iida. Last updated 5 months ago.
geneexpressionsinglecellsequencingclusteringgenesignalingcpp
7.7 match 4.32 score 21 scriptsbioc
gwascat:representing and modeling data in the EMBL-EBI GWAS catalog
Represent and model data in the EMBL-EBI GWAS catalog.
Maintained by VJ Carey. Last updated 5 months ago.
5.4 match 6.05 score 110 scripts 2 dependentsbioc
tenXplore:ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics
Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics.
Maintained by VJ Carey. Last updated 5 months ago.
immunooncologydimensionreductionprincipalcomponenttranscriptomicssinglecell
7.7 match 4.18 score 7 scriptsbioc
Damsel:Damsel: an end to end analysis of DamID
Damsel provides an end to end analysis of DamID data. Damsel takes bam files from Dam-only control and fusion samples and counts the reads matching to each GATC region. edgeR is utilised to identify regions of enrichment in the fusion relative to the control. Enriched regions are combined into peaks, and are associated with nearby genes. Damsel allows for IGV style plots to be built as the results build, inspired by ggcoverage, and using the functionality and layering ability of ggplot2. Damsel also conducts gene ontology testing with bias correction through goseq, and future versions of Damsel will also incorporate motif enrichment analysis. Overall, Damsel is the first package allowing for an end to end analysis with visual capabilities. The goal of Damsel was to bring all the analysis into one place, and allow for exploratory analysis within R.
Maintained by Caitlin Page. Last updated 5 months ago.
differentialmethylationpeakdetectiongenepredictiongenesetenrichment
5.9 match 5.34 score 20 scriptsbioc
bugsigdbr:R-side access to published microbial signatures from BugSigDB
The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.
Maintained by Ludwig Geistlinger. Last updated 8 days ago.
dataimportgenesetenrichmentmetagenomicsmicrobiomebioconductor-package
4.8 match 3 stars 6.46 score 48 scriptsbioc
TADCompare:TADCompare: Identification and characterization of differential TADs
TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results.
Maintained by Mikhail Dozmorov. Last updated 5 months ago.
softwarehicsequencingfeatureextractionclustering
4.3 match 23 stars 7.04 score 10 scriptsbioc
SpliceWiz:interactive analysis and visualization of alternative splicing in R
The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.
Maintained by Alex Chit Hei Wong. Last updated 3 days ago.
softwaretranscriptomicsrnaseqalternativesplicingcoveragedifferentialsplicingdifferentialexpressionguisequencingcppopenmp
4.6 match 16 stars 6.41 score 8 scriptsbioc
GOpro:Find the most characteristic gene ontology terms for groups of human genes
Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng).
Maintained by Lidia Chrabaszcz. Last updated 5 months ago.
annotationclusteringgogeneexpressiongenesetenrichmentmultiplecomparisoncpp
7.9 match 2 stars 3.60 score 4 scripts
bioc
famat:Functional analysis of metabolic and transcriptomic data
Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process.
Maintained by Mathieu Charles. Last updated 5 months ago.
functionalpredictiongenesetenrichmentpathwaysgoreactomekeggcompoundgene-ontologygenesshiny
7.5 match 1 stars 3.78 score 2 scriptsbioc
simplifyEnrichment:Simplify Functional Enrichment Results
A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings.
Maintained by Zuguang Gu. Last updated 5 months ago.
softwarevisualizationgoclusteringgenesetenrichment
3.5 match 113 stars 8.02 score 196 scriptsbioc
mgsa:Model-based gene set analysis
Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology.
Maintained by Sebastian Bauer. Last updated 5 months ago.
pathwaysgogenesetenrichmentopenmp
4.3 match 5 stars 6.08 score 12 scriptsbioc
Rcpi:Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery
A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery.
Maintained by Nan Xiao. Last updated 5 months ago.
softwaredataimportdatarepresentationfeatureextractioncheminformaticsbiomedicalinformaticsproteomicsgosystemsbiologybioconductorbioinformaticsdrug-discoveryfeature-extractionfingerprintmolecular-descriptorsprotein-sequences
3.4 match 37 stars 7.81 score 29 scripts
egeulgen
pathfindR.data:Data Package for 'pathfindR'
This is a data-only package, containing data needed to run the CRAN package 'pathfindR', a package for enrichment analysis utilizing active subnetworks. This package contains protein-protein interaction network data, data related to gene sets and example input/output data.
Maintained by Ege Ulgen. Last updated 11 months ago.
6.0 match 4.21 score 1 scripts 1 dependents
mpallocc
autoGO:Auto-GO: Reproducible, Robust and High Quality Ontology Enrichment Visualizations
Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.
Maintained by Fabio Ticconi. Last updated 19 days ago.
6.1 match 2 stars 4.08 score
dwulff
text2sdg:Detecting UN Sustainable Development Goals in Text
The United Nations’ Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information regarding the methodology see Meier, Mata & Wulff (2022) <arXiv:2110.05856>.
Maintained by Dominik S. Meier. Last updated 6 months ago.
natural-language-processingsustainabilitysustainable-developmentsustainable-development-goals
4.0 match 18 stars 6.13 score 9 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 26 days ago.
dnamethylationdifferentialmethylationgeneregulationgeneexpressionmethylationarraydifferentialexpressionpathwaysnetworksequencingsurvivalsoftwarebiocbioconductorgdcintegrative-analysistcgatcga-datatcgabiolinks
1.7 match 305 stars 14.45 score 1.6k scripts 6 dependentsbioc
edgeR:Empirical Analysis of Digital Gene Expression Data in R
Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.
Maintained by Yunshun Chen. Last updated 5 days ago.
alternativesplicingbatcheffectbayesianbiomedicalinformaticscellbiologychipseqclusteringcoveragedifferentialexpressiondifferentialmethylationdifferentialsplicingdnamethylationepigeneticsfunctionalgenomicsgeneexpressiongenesetenrichmentgeneticsimmunooncologymultiplecomparisonnormalizationpathwaysproteomicsqualitycontrolregressionrnaseqsagesequencingsinglecellsystemsbiologytimecoursetranscriptiontranscriptomicsopenblas
1.7 match 13.40 score 17k scripts 255 dependentsbioc
clusterProfiler:A universal enrichment tool for interpreting omics data
This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions.
Maintained by Guangchuang Yu. Last updated 4 months ago.
annotationclusteringgenesetenrichmentgokeggmultiplecomparisonpathwaysreactomevisualizationenrichment-analysisgsea
1.3 match 1.1k stars 17.03 score 11k scripts 48 dependentsbioc
BgeeDB:Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology
A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns.
Maintained by Julien Wollbrett. Last updated 5 months ago.
softwaredataimportsequencinggeneexpressionmicroarraygogenesetenrichmentbioinformaticsenrichment-analysisrna-seqscrna-seqsingle-cell
2.7 match 15 stars 8.46 score 19 scripts 1 dependents
rudjer
SparseM:Sparse Linear Algebra
Some basic linear algebra functionality for sparse matrices is provided: including Cholesky decomposition and backsolving as well as standard R subsetting and Kronecker products.
Maintained by Roger Koenker. Last updated 8 months ago.
1.9 match 3 stars 11.47 score 306 scripts 1.5k dependentsropensci
mregions2:Access Data from Marineregions.org: Gazetteer & Data Products
Explore and retrieve marine geospatial data from the Marine Regions Gazetteer <https://marineregions.org/gazetteer.php?p=webservices> and the Marine Regions Data Products <https://marineregions.org/webservices.php>.
Maintained by Salvador Jesús Fernández Bejarano. Last updated 2 days ago.
3.6 match 9 stars 5.97 score 40 scriptsbioc
geneXtendeR:Optimized Functional Annotation Of ChIP-seq Data
geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.
Maintained by Bohdan Khomtchouk. Last updated 5 months ago.
chipseqgeneticsannotationgenomeannotationdifferentialpeakcallingcoveragepeakdetectionchiponchiphistonemodificationdataimportnaturallanguageprocessingvisualizationgosoftwarebioconductorbioinformaticscchip-seqcomputational-biologyepigeneticsfunctional-annotation
5.3 match 9 stars 3.95 score 5 scriptsbioc
categoryCompare:Meta-analysis of high-throughput experiments using feature annotations
Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).
Maintained by Robert M. Flight. Last updated 5 months ago.
annotationgomultiplecomparisonpathwaysgeneexpressionbioconductor
3.0 match 6 stars 6.68 scorebioc
EGAD:Extending guilt by association by degree
The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods.
Maintained by Sara Ballouz. Last updated 5 months ago.
softwarefunctionalgenomicssystemsbiologygenepredictionfunctionalpredictionnetworkenrichmentgraphandnetworknetwork
4.0 match 4.92 score 83 scriptsbioc
annaffy:Annotation tools for Affymetrix biological metadata
Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria.
Maintained by Colin A. Smith. Last updated 5 months ago.
onechannelmicroarrayannotationgopathwaysreportwriting
3.5 match 5.64 score 60 scripts 3 dependents
emilhvitfeldt
textdata:Download and Load Various Text Datasets
Provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.
Maintained by Emil Hvitfeldt. Last updated 10 months ago.
2.0 match 75 stars 9.66 score 1.4k scripts 1 dependents
welch-lab
rliger:Linked Inference of Genomic Experimental Relationships
Uses an extension of nonnegative matrix factorization to identify shared and dataset-specific factors. See Welch J, Kozareva V, et al (2019) <doi:10.1016/j.cell.2019.05.006>, and Liu J, Gao C, Sodicoff J, et al (2020) <doi:10.1038/s41596-020-0391-8> for more details.
Maintained by Yichen Wang. Last updated 2 months ago.
nonnegative-matrix-factorizationsingle-cellopenblascpp
1.7 match 408 stars 10.77 score 334 scripts 1 dependentsbioc
evaluomeR:Evaluation of Bioinformatics Metrics
Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics.
Maintained by José Antonio Bernabé-Díaz. Last updated 5 months ago.
clusteringclassificationfeatureextractionassessmentclustering-evaluationevaluomeevaluomermetrics
3.8 match 4.82 score 33 scripts
iohprofiler
IOHanalyzer:Data Analysis Part of 'IOHprofiler'
The data analysis module for the Iterative Optimization Heuristics Profiler ('IOHprofiler'). This module provides statistical analysis methods for the benchmark data generated by optimization heuristics, which can be visualized through a web-based interface. The benchmark data is usually generated by the experimentation module, called 'IOHexperimenter'. 'IOHanalyzer' also supports the widely used 'COCO' (Comparing Continuous Optimisers) data format for benchmarking.
Maintained by Diederick Vermetten. Last updated 10 months ago.
3.4 match 24 stars 5.10 score 13 scriptsbioc
iSEEpathways:iSEE extension for panels related to pathway analysis
This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of pathway analysis results. This package does not perform pathway analysis. Instead, it provides methods to embed precomputed pathway analysis results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications.
Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.
softwareinfrastructuredifferentialexpressiongeneexpressionguivisualizationpathwaysgenesetenrichmentgoshinyappsbioconductorhacktoberfestiseeiseeu
3.3 match 1 stars 4.95 score 10 scriptsbioc
rrvgo:Reduce + Visualize GO
Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity.
Maintained by Sergi Sayols. Last updated 5 months ago.
annotationclusteringgonetworkpathwayssoftware
2.0 match 24 stars 7.74 score 190 scripts
kalifa-manjang
GOxploreR:Structural Exploration of the Gene Ontology (GO) Knowledge Base
It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). Given a set of genes, the package contains functions to assess the GO and obtain the terms associated with the genes and the levels of the GO terms. The package provides functions for the three different GO ontology. We discussed the methods explicitly in the following article <doi:10.1038/s41598-020-73326-3>.
Maintained by Kalifa Manjang. Last updated 1 years ago.
7.0 match 2.26 score 18 scriptspapatheodorou-group
scGOclust:Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq
Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.
Maintained by Yuyao Song. Last updated 1 years ago.
3.3 match 9 stars 4.80 score 14 scriptsbioc
consICA:consensus Independent Component Analysis
consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.
Maintained by Petr V. Nazarov. Last updated 5 months ago.
technologystatisticalmethodsequencingrnaseqtranscriptomicsclassificationfeatureextraction
3.6 match 4.30 score 2 scripts
cyrillagger
scDiffCom:Differential Analysis of Intercellular Communication from scRNA-Seq Data
Analysis tools to investigate changes in intercellular communication from scRNA-seq data. Using a Seurat object as input, the package infers which cell-cell interactions are present in the dataset and how these interactions change between two conditions of interest (e.g. young vs old). It relies on an internal database of ligand-receptor interactions (available for human, mouse and rat) that have been gathered from several published studies. Detection and differential analyses rely on permutation tests. The package also contains several tools to perform over-representation analysis and visualize the results. See Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full description of the methodology.
Maintained by Cyril Lagger. Last updated 1 years ago.
3.6 match 21 stars 4.25 score 17 scriptsbioc
gage:Generally Applicable Gene-set Enrichment for Pathway Analysis
GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.
Maintained by Weijun Luo. Last updated 5 months ago.
pathwaysgodifferentialexpressionmicroarrayonechanneltwochannelrnaseqgeneticsmultiplecomparisongenesetenrichmentgeneexpressionsystemsbiologysequencing
1.7 match 5 stars 8.71 score 784 scripts 1 dependents
bioc
GraphAT:Graph Theoretic Association Tests
Functions and data used in Balasubramanian, et al. (2004)
Maintained by Thomas LaFramboise. Last updated 5 months ago.
6.5 match 2.30 score 4 scriptsbioc
SVMDO:Identification of Tumor-Discriminating mRNA Signatures via Support Vector Machines Supported by Disease Ontology
It is an easy-to-use GUI using disease information for detecting tumor/normal sample discriminating gene sets from differentially expressed genes. Our approach is based on an iterative algorithm filtering genes with disease ontology enrichment analysis and wilk and wilks lambda criterion connected to SVM classification model construction. Along with gene set extraction, SVMDO also provides individual prognostic marker detection. The algorithm is designed for FPKM and RPKM normalized RNA-Seq transcriptome datasets.
Maintained by Mustafa Erhan Ozer. Last updated 5 months ago.
genesetenrichmentdifferentialexpressionguiclassificationrnaseqtranscriptomicssurvivalmachine-learningrna-seqshiny
3.2 match 4.60 score 2 scriptsbioc
transcriptogramer:Transcriptional analysis based on transcriptograms
R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered.
Maintained by Diego Morais. Last updated 5 months ago.
softwarenetworkvisualizationsystemsbiologygeneexpressiongenesetenrichmentgraphandnetworkclusteringdifferentialexpressionmicroarrayrnaseqtranscriptionimmunooncology
3.0 match 4 stars 4.81 score 9 scriptsbioc
BAGS:A Bayesian Approach for Geneset Selection
R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest.
Maintained by Alejandro Quiroz-Zarate. Last updated 5 months ago.
3.3 match 4.38 score 40 scripts
civisanalytics
civis:R Client for the 'Civis Platform API'
A convenient interface for making requests directly to the 'Civis Platform API' <https://www.civisanalytics.com/platform/>. Full documentation available 'here' <https://civisanalytics.github.io/civis-r/>.
Maintained by Peter Cooman. Last updated 2 months ago.
1.8 match 16 stars 7.84 score 144 scriptsbioc
gsean:Gene Set Enrichment Analysis with Networks
Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis.
Maintained by Dongmin Jung. Last updated 5 months ago.
softwarestatisticalmethodnetworkgraphandnetworkgenesetenrichmentgeneexpressionnetworkenrichmentpathwaysdifferentialexpression
3.4 match 4.00 score 1 scripts
icarda-git
QBMS:Query the Breeding Management System(s)
This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like 'EBS' <https://ebs.excellenceinbreeding.org/>, 'BMS' <https://bmspro.io>, 'BreedBase' <https://breedbase.org>, and 'GIGWA' <https://github.com/SouthGreenPlatform/Gigwa2> (using 'BrAPI' <https://brapi.org> calls). Extra helper functions support environmental data sources, including 'TerraClimate' <https://www.climatologylab.org/terraclimate.html> and 'FAO' 'HWSDv2' <https://gaez.fao.org/pages/hwsd> soil database.
Maintained by Khaled Al-Shamaa. Last updated 6 months ago.
1.7 match 8 stars 7.85 score 33 scripts 1 dependentsropensci
rppo:Access the Global Plant Phenology Data Portal
Search plant phenology data aggregated from several sources and available on the Global Plant Phenology Data Portal.
Maintained by John Deck. Last updated 2 years ago.
3.6 match 3 stars 3.69 score 11 scripts
proteomicslab57357
UniprotR:Retrieving Information of Proteins from Uniprot
Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
Maintained by Mohamed Soudy. Last updated 2 years ago.
1.7 match 61 stars 7.65 score 89 scripts 1 dependentsbioc
CardinalIO:Read and write mass spectrometry imaging files
Fast and efficient reading and writing of mass spectrometry imaging data files. Supports imzML and Analyze 7.5 formats. Provides ontologies for mass spectrometry imaging.
Maintained by Kylie Ariel Bemis. Last updated 5 months ago.
softwareinfrastructuredataimportmassspectrometryimagingmassspectrometrycpp
2.4 match 1 stars 5.35 score 3 scripts 1 dependentsbioc
hipathia:HiPathia: High-throughput Pathway Analysis
Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway.
Maintained by Marta R. Hidalgo. Last updated 5 months ago.
pathwaysgraphandnetworkgeneexpressiongenesignalinggo
3.4 match 3.62 score 42 scripts
ediorg
ecocomDP:Tools to Create, Use, and Convert ecocomDP Data
Work with the Ecological Community Data Design Pattern. 'ecocomDP' is a flexible data model for harmonizing ecological community surveys, in a research question agnostic format, from source data published across repositories, and with methods that keep the derived data up-to-date as the underlying sources change. Described in O'Brien et al. (2021), <doi:10.1016/j.ecoinf.2021.101374>.
Maintained by Colin Smith. Last updated 7 months ago.
1.5 match 32 stars 8.22 score 77 scriptsbioc
esATAC:An Easy-to-use Systematic pipeline for ATACseq data analysis
This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly.
Maintained by Zheng Wei. Last updated 5 months ago.
immunooncologysequencingdnaseqqualitycontrolalignmentpreprocessingcoverageatacseqdnaseseqatac-seqbioconductorpipelinecppopenjdk
2.0 match 23 stars 6.11 score 3 scriptsbioc
KnowSeq:KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline
KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study.
Maintained by Daniel Castillo-Secilla. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentdataimportclassificationfeatureextractionsequencingrnaseqbatcheffectnormalizationpreprocessingqualitycontrolgeneticstranscriptomicsmicroarrayalignmentpathwayssystemsbiologygoimmunooncology
3.7 match 3.30 score 5 scriptsbioc
goTools:Functions for Gene Ontology database
Wraper functions for description/comparison of oligo ID list using Gene Ontology database
Maintained by Agnes Paquet. Last updated 5 months ago.
3.7 match 3.30 score 1 scriptsbioc
zenith:Gene set analysis following differential expression using linear (mixed) modeling with dream
Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream().
Maintained by Gabriel Hoffman. Last updated 5 months ago.
rnaseqgeneexpressiongenesetenrichmentdifferentialexpressionbatcheffectqualitycontrolregressionepigeneticsfunctionalgenomicstranscriptomicsnormalizationpreprocessingmicroarrayimmunooncologysoftware
1.9 match 6.39 score 91 scripts 1 dependents
fcampelo
CALANGO:Comparative Analysis with Annotation-Based Genomic Components
A first-principle, phylogeny-aware comparative genomics tool for investigating associations between terms used to annotate genomic components (e.g., Pfam IDs, Gene Ontology terms,) with quantitative or rank variables such as number of cell types, genome size, or density of specific genomic elements. See the project website for more information, documentation and examples, and <doi:10.1016/j.patter.2023.100728> for the full paper.
Maintained by Felipe Campelo. Last updated 6 months ago.
2.5 match 2 stars 4.60 score 4 scriptsbioc
mosdef:MOSt frequently used and useful Differential Expression Functions
This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports.
Maintained by Federico Marini. Last updated 3 months ago.
geneexpressionsoftwaretranscriptiontranscriptomicsdifferentialexpressionvisualizationreportwritinggenesetenrichmentgo
1.9 match 6.12 score 4 dependentsbioc
tricycle:tricycle: Transferable Representation and Inference of cell cycle
The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference.
Maintained by Shijie Zheng. Last updated 5 months ago.
singlecellsoftwaretranscriptomicsrnaseqtranscriptionbiologicalquestiondimensionreductionimmunooncology
1.7 match 24 stars 6.52 score 46 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
2.4 match 4.60 score 4 scripts
ganglilab
geneset:Get Gene Sets for Gene Enrichment Analysis
Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>), 'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>), 'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>). Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of terms ID and term description.
Maintained by Yunze Liu. Last updated 2 years ago.
enrichment-analysisgenegeneset-enrichment
2.3 match 9 stars 4.75 score 21 scripts 2 dependents
mrcieu
epigraphdb:Interface Package for the 'EpiGraphDB' Platform
The interface package to access data from the 'EpiGraphDB' <https://epigraphdb.org> platform. It provides easy access to the 'EpiGraphDB' platform with functions that query the corresponding REST endpoints on the API <https://api.epigraphdb.org> and return the response data in the 'tibble' data frame format.
Maintained by Yi Liu. Last updated 3 years ago.
api-clientbioinformaticsepidemiologygraph-databasemendelian-randomizationphenotypes
1.8 match 27 stars 6.02 score 13 scripts
thej022214
corHMM:Hidden Markov Models of Character Evolution
Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.
Maintained by Jeremy Beaulieu. Last updated 27 days ago.
1.1 match 12 stars 9.48 score 422 scripts 2 dependentsbioc
knowYourCG:Functional analysis of DNA methylome datasets
KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks.
Maintained by Goldberg David. Last updated 2 months ago.
epigeneticsdnamethylationsequencingsinglecellspatialmethylationarrayzlib
1.7 match 2 stars 6.10 score 4 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
1.7 match 6.07 score 117 scriptsbioc
SemDist:Information Accretion-based Function Predictor Evaluation
This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor.
Maintained by Ian Gonzalez. Last updated 5 months ago.
classificationannotationgosoftware
2.4 match 1 stars 4.30 score 3 scriptsbioc
safe:Significance Analysis of Function and Expression
SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.
Maintained by Ludwig Geistlinger. Last updated 5 months ago.
differentialexpressionpathwaysgenesetenrichmentstatisticalmethodsoftware
1.8 match 5.60 score 32 scripts 5 dependents
ms-quality-hub
rmzqc:Creation, Reading and Validation of 'mzqc' Files
Reads, writes and validates 'mzQC' files. The 'mzQC' format is a standardized file format for the exchange, transmission, and archiving of quality metrics derived from biological mass spectrometry data, as defined by the HUPO-PSI (Human Proteome Organisation - Proteomics Standards Initiative) Quality Control working group. See <https://hupo-psi.github.io/mzQC/> for details.
Maintained by Chris Bielow. Last updated 11 months ago.
hacktoberfestmass-spectrometrymzqcquality-control
1.7 match 2 stars 5.73 score 10 scripts 3 dependents
january3
tmod:Feature Set Enrichment Analysis for Metabolomics and Transcriptomics
Methods and feature set definitions for feature or gene set enrichment analysis in transcriptional and metabolic profiling data. Package includes tests for enrichment based on ranked lists of features, functions for visualisation and multivariate functional analysis. See Zyla et al (2019) <doi:10.1093/bioinformatics/btz447>.
Maintained by January Weiner. Last updated 2 months ago.
1.3 match 3 stars 6.88 score 168 scripts 1 dependentsbioc
DAPAR:Tools for the Differential Analysis of Proteins Abundance with R
The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package).
Maintained by Samuel Wieczorek. Last updated 5 months ago.
proteomicsnormalizationpreprocessingmassspectrometryqualitycontrolgodataimportprostar1
1.7 match 2 stars 5.42 score 22 scripts 1 dependentsdaniel-jg
gsEasy:Gene Set Enrichment Analysis in R
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Maintained by Daniel Greene. Last updated 1 years ago.
4.5 match 2.00 score 9 scriptsbioc
GenomicInteractionNodes:A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data
The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation.
Maintained by Jianhong Ou. Last updated 1 months ago.
1.9 match 4.70 score 1 scriptsbioc
rTRM:Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks
rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.
Maintained by Diego Diez. Last updated 5 months ago.
transcriptionnetworkgeneregulationgraphandnetworkbioconductorbioinformatics
1.7 match 3 stars 4.86 score 3 scripts 1 dependentsbioc
fenr:Fast functional enrichment for interactive applications
Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages.
Maintained by Marek Gierlinski. Last updated 24 days ago.
functionalpredictiondifferentialexpressiongenesetenrichmentgokeggreactomeproteomics
1.8 match 4.60 score 4 scriptsbioc
annotationTools:Annotate microarrays and perform cross-species gene expression analyses using flat file databases
Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files).
Maintained by Alexandre Kuhn. Last updated 5 months ago.
1.8 match 4.51 score 18 scripts 1 dependentsbioc
CeTF:Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis
This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data).
Maintained by Carlos Alberto Oliveira de Biagi Junior. Last updated 5 months ago.
sequencingrnaseqmicroarraygeneexpressiontranscriptionnormalizationdifferentialexpressionsinglecellnetworkregressionchipseqimmunooncologycoveragecpp
1.8 match 4.30 score 9 scriptsbioc
cellxgenedp:Discover and Access Single Cell Data Sets in the CELLxGENE Data Portal
The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading.
Maintained by Martin Morgan. Last updated 5 months ago.
singlecelldataimportthirdpartyclient
1.2 match 8 stars 6.64 score 27 scriptsropensci
BaseSet:Working with Sets the Tidy Way
Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a "tidy" way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.
Maintained by Lluís Revilla Sancho. Last updated 25 days ago.
bioconductorbioconductor-packagesets
1.3 match 11 stars 5.69 score 5 scripts
patzaw
ReDaMoR:Relational Data Modeler
The aim of this package is to manipulate relational data models in R. It provides functions to create, modify and export data models in json format. It also allows importing models created with 'MySQL Workbench' (<https://www.mysql.com/products/workbench/>). These functions are accessible through a graphical user interface made with 'shiny'. Constraints such as types, keys, uniqueness and mandatory fields are automatically checked and corrected when editing a model. Finally, real data can be confronted to a model to check their compatibility.
Maintained by Patrice Godard. Last updated 23 days ago.
1.2 match 17 stars 6.24 score 17 scripts 1 dependentsbioc
gCrisprTools:Suite of Functions for Pooled Crispr Screen QC and Analysis
Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity.
Maintained by Russell Bainer. Last updated 5 months ago.
immunooncologycrisprpooledscreensexperimentaldesignbiomedicalinformaticscellbiologyfunctionalgenomicspharmacogenomicspharmacogeneticssystemsbiologydifferentialexpressiongenesetenrichmentgeneticsmultiplecomparisonnormalizationpreprocessingqualitycontrolrnaseqregressionsoftwarevisualization
1.5 match 4.78 score 8 scriptsdaniel-jg
SimReg:Similarity Regression
Similarity regression, evaluating the probability of association between sets of ontological terms and binary response vector. A no-association model is compared with one in which the log odds of a true response is linked to the semantic similarity between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.
Maintained by Daniel Greene. Last updated 1 years ago.
2.5 match 2.78 score 6 scripts
maialab
mskcc.oncotree:Interface to the 'OncoTree' API
Programmatic access to 'OncoTree' API <http://oncotree.mskcc.org/>. Get access to tumor main types, identifiers and utility routines to map across to other tumor classification systems.
Maintained by Ramiro Magno. Last updated 2 years ago.
1.8 match 6 stars 3.48 score 7 scripts
jinghuazhao
gaawr2:Genetic Association Analysis
It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by Zhao (2009, <doi:10.69645/DCRY5578>), which showcases analysis in aspects such as testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models as well as Bayesian implementation, power calculation in study design and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications.
Maintained by Jing Hua Zhao. Last updated 2 days ago.
1.3 match 4.90 score
raghvendra5688
func2vis:Clean and Visualize Over Expression Results from 'ConsensusPathDB'
Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms, protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB' from gene set over-expression analysis. Performs clustering of pathway based on similarity of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA) when up and down regulated genes are known. The methods are described in a paper currently submitted by Orecchioni et al, 2020 in Nanoscale.
Maintained by Raghvendra Mall. Last updated 2 years ago.
5.8 match 1.00 score 2 scriptsbioc
CelliD:Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis
CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data.
Maintained by Akira Cortal. Last updated 5 months ago.
rnaseqsinglecelldimensionreductionclusteringgenesetenrichmentgeneexpressionatacseqopenblascppopenmp
1.1 match 4.85 score 70 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
1.2 match 1 stars 4.48 score 7 scripts 1 dependentsbioc
MLP:Mean Log P Analysis
Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available.
Maintained by Tobias Verbeke. Last updated 5 months ago.
geneticsgeneexpressionpathwaysreactomekegggo
0.8 match 4.78 score 4 scripts 1 dependents
cran
MARVEL:Revealing Splicing Dynamics at Single-Cell Resolution
Alternative splicing represents an additional and underappreciated layer of complexity underlying gene expression profiles. Nevertheless, there remains hitherto a paucity of software to investigate splicing dynamics at single-cell resolution. 'MARVEL' enables splicing analysis of single-cell RNA-sequencing data generated from plate- and droplet-based library preparation methods.
Maintained by Sean Wen. Last updated 2 years ago.
1.3 match 2.71 score 51 scriptsbioc
goSTAG:A tool to use GO Subtrees to Tag and Annotate Genes within a set
Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster.
Maintained by Brian D. Bennett. Last updated 5 months ago.
geneexpressiondifferentialexpressiongenesetenrichmentclusteringmicroarraymrnamicroarrayrnaseqvisualizationgoimmunooncology
0.8 match 4.30 score 1 scripts
dzhakparov
GeneSelectR:Comprehensive Feature Selection Worfkflow for Bulk RNAseq Datasets
GeneSelectR is a versatile R package designed for efficient RNA sequencing data analysis. Its key innovation lies in the seamless integration of the Python sklearn machine learning framework with R-based bioinformatics tools. This integration enables GeneSelectR to perform robust ML-driven feature selection while simultaneously leveraging the power of Gene Ontology (GO) enrichment and semantic similarity analyses. By combining these diverse methodologies, GeneSelectR offers a comprehensive workflow that optimizes both the computational aspects of ML and the biological insights afforded by advanced bioinformatics analyses. Ideal for researchers in bioinformatics, GeneSelectR stands out as a unique tool for analyzing complex RNAseq datasets with enhanced precision and relevance.
Maintained by Damir Zhakparov. Last updated 10 months ago.
0.5 match 19 stars 5.28 score 7 scriptsbioc
InterCellar:InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics
InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions.
Maintained by Marta Interlandi. Last updated 5 months ago.
softwaresinglecellvisualizationgotranscriptomics
0.5 match 9 stars 4.95 score 7 scriptsbioc
immunotation:Tools for working with diverse immune genes
MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database.
Maintained by Katharina Imkeller. Last updated 5 months ago.
softwareimmunooncologybiomedicalinformaticsgeneticsannotation
0.5 match 8 stars 4.90 score 3 scriptsbioc
ADAM:ADAM: Activity and Diversity Analysis Module
ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes).
Maintained by Jose Luiz Rybarczyk Filho. Last updated 5 months ago.
genesetenrichmentpathwayskegggeneexpressionmicroarraycpp
0.5 match 4.78 score 8 scripts 1 dependentscran
Platypus:Single-Cell Immune Repertoire and Gene Expression Analysis
We present 'Platypus', an open-source software platform providing a user-friendly interface to investigate B-cell receptor and T-cell receptor repertoires from scSeq experiments. 'Platypus' provides a framework to automate and ease the analysis of single-cell immune repertoires while also incorporating transcriptional information involving unsupervised clustering, gene expression and gene ontology. This R version of 'Platypus' is part of the 'ePlatypus' ecosystem for computational analysis of immunogenomics data: Yermanos et al. (2021) <doi:10.1093/nargab/lqab023>, Cotet et al. (2023) <doi:10.1093/bioinformatics/btad553>.
Maintained by Alexander Yermanos. Last updated 5 months ago.
0.5 match 4.58 score 38 scripts
hanjunwei-lab
CITMIC:Estimation of Cell Infiltration Based on Cell Crosstalk
A systematic biology tool was developed to identify cell infiltration via an Individualized Cell crosstalk network. 'CITMIC' first constructed a weighted cell crosstalk network by integrating Cell-target interaction information, biological process data from the Gene Ontology (GO) database, and gene transcriptomic data in a specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.
Maintained by Junwei Han. Last updated 6 months ago.
0.5 match 1 stars 4.48 score 5 scriptsbioc
GSEAmining:Make Biological Sense of Gene Set Enrichment Analysis Outputs
Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study.
Maintained by Oriol Arqués. Last updated 5 months ago.
genesetenrichmentclusteringvisualization
0.5 match 4.00 score 7 scriptscran
Rdiagnosislist:Manipulate SNOMED CT Diagnosis Lists
Functions and methods for manipulating 'SNOMED CT' concepts. The package contains functions for loading the 'SNOMED CT' release into a convenient R environment, selecting 'SNOMED CT' concepts using regular expressions, and navigating the 'SNOMED CT' ontology. It provides the 'SNOMEDconcept' S3 class for a vector of 'SNOMED CT' concepts (stored as 64-bit integers) and the 'SNOMEDcodelist' S3 class for a table of concepts IDs with descriptions. The package can be used to construct sets of 'SNOMED CT' concepts for research (<doi:10.1093/jamia/ocac158>). For more information about 'SNOMED CT' visit <https://www.snomed.org/>.
Maintained by Anoop D. Shah. Last updated 2 months ago.
0.5 match 1 stars 3.60 scorebioc
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 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.
0.5 match 2.00 score 2 scripts
dzhang777
SlideCNA:Calls Copy Number Alterations from Slide-Seq Data
This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about 'SlideCNA' is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package 'enrichR' (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").
Maintained by Diane Zhang. Last updated 2 months ago.
0.5 match 1.70 score 3 scriptskalifa-manjang
gontr:Dataset for 'GOxploreR'
Contains the Gene ontology terms and skeleton for the reduced GO directed acyclic graph (DAG) for the organisms Rat and Mouse. The methods are explicitly discussed in the following article : Manjang et al (2020) <doi:10.1038/s41598-020-73326-3>.
Maintained by Kalifa Manjang. Last updated 4 years ago.
0.5 match 1.48 score 1 dependents
link-ny
OncoTree:Expose the OncoTree API for R
Expose the OncoTree API for R. OncoTree is a standardized ontology of cancer terms that is used to annotate cancer data. This package provides a set of functions to query the OncoTree API and retrieve information about cancer types, subtypes, and their relationships.
Maintained by Marcel Ramos. Last updated 11 months ago.
0.5 match 1.28 score 19 scriptscran
OnboardClient:Bindings for Onboard Data's Building Data API
Provides a wrapper for the Onboard Data building data API <https://api.onboarddata.io/swagger>. Along with streamlining access to the API, this package simplifies access to sensor time series data, metadata (sensors, equipment, and buildings), and details about the Onboard data model/ontology.
Maintained by Christopher Dudas-Thomas. Last updated 2 years ago.
0.5 match 1.00 score