Showing 30 of total 30 results (show query)
bioc
mixOmics:Omics Data Integration Project
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Maintained by Eva Hamrud. Last updated 2 days ago.
immunooncologymicroarraysequencingmetabolomicsmetagenomicsproteomicsgenepredictionmultiplecomparisonclassificationregressionbioconductorgenomicsgenomics-datagenomics-visualizationmultivariate-analysismultivariate-statisticsomicsr-pkgr-project
185 stars 13.75 score 1.3k scripts 22 dependentsbioc
DECIPHER:Tools for curating, analyzing, and manipulating biological sequences
A toolset for deciphering and managing biological sequences.
Maintained by Erik Wright. Last updated 18 days ago.
clusteringgeneticssequencingdataimportvisualizationmicroarrayqualitycontrolqpcralignmentwholegenomemicrobiomeimmunooncologygenepredictionopenmp
10.55 score 1.1k scripts 14 dependentsbioc
IsoformSwitchAnalyzeR:Identify, Annotate and Visualize Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data
Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc.
Maintained by Kristoffer Vitting-Seerup. Last updated 5 months ago.
geneexpressiontranscriptionalternativesplicingdifferentialexpressiondifferentialsplicingvisualizationstatisticalmethodtranscriptomevariantbiomedicalinformaticsfunctionalgenomicssystemsbiologytranscriptomicsrnaseqannotationfunctionalpredictiongenepredictiondataimportmultiplecomparisonbatcheffectimmunooncology
108 stars 9.26 score 125 scriptsbioc
pathwayPCA:Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection
pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>; Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
Maintained by Gabriel Odom. Last updated 5 months ago.
copynumbervariationdnamethylationgeneexpressionsnptranscriptiongenepredictiongenesetenrichmentgenesignalinggenetargetgenomewideassociationgenomicvariationcellbiologyepigeneticsfunctionalgenomicsgeneticslipidomicsmetabolomicsproteomicssystemsbiologytranscriptomicsclassificationdimensionreductionfeatureextractionprincipalcomponentregressionsurvivalmultiplecomparisonpathways
11 stars 7.74 score 42 scriptsbioc
coRdon:Codon Usage Analysis and Prediction of Gene Expressivity
Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results.
Maintained by Anamaria Elek. Last updated 5 months ago.
softwaremetagenomicsgeneexpressiongenesetenrichmentgenepredictionvisualizationkeggpathwaysgenetics cellbiologybiomedicalinformaticsimmunooncology
20 stars 7.71 score 48 scripts 1 dependentsbioc
AlphaMissenseR:Accessing AlphaMissense Data Resources in R
The AlphaMissense publication <https://www.science.org/doi/epdf/10.1126/science.adg7492> outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo <https://zenodo.org/record/10813168> include, for instance, 71M variants across hg19 and hg38 genome builds. The 'AlphaMissenseR' package allows ready access to the data, downloading individual files to DuckDB databases for exploration and integration into *R* and *Bioconductor* workflows.
Maintained by Martin Morgan. Last updated 5 months ago.
snpannotationfunctionalgenomicsstructuralpredictiontranscriptomicsvariantannotationgenepredictionimmunooncology
8 stars 6.82 score 10 scriptsbioc
iNETgrate:Integrates DNA methylation data with gene expression in a single gene network
The iNETgrate package provides functions to build a correlation network in which nodes are genes. DNA methylation and gene expression data are integrated to define the connections between genes. This network is used to identify modules (clusters) of genes. The biological information in each of the resulting modules is represented by an eigengene. These biological signatures can be used as features e.g., for classification of patients into risk categories. The resulting biological signatures are very robust and give a holistic view of the underlying molecular changes.
Maintained by Habil Zare. Last updated 5 months ago.
geneexpressionrnaseqdnamethylationnetworkinferencenetworkgraphandnetworkbiomedicalinformaticssystemsbiologytranscriptomicsclassificationclusteringdimensionreductionprincipalcomponentmrnamicroarraynormalizationgenepredictionkeggsurvivalcore-services
74 stars 6.21 score 1 scriptsbioc
timeOmics:Time-Course Multi-Omics data integration
timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.
Maintained by Antoine Bodein. Last updated 5 months ago.
clusteringfeatureextractiontimecoursedimensionreductionsoftwaresequencingmicroarraymetabolomicsmetagenomicsproteomicsclassificationregressionimmunooncologygenepredictionmultiplecomparisonclusterintegrationmulti-omicstime-series
24 stars 5.98 score 10 scriptsbioc
circRNAprofiler:circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs
R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis.
Maintained by Simona Aufiero. Last updated 5 months ago.
annotationstructuralpredictionfunctionalpredictiongenepredictiongenomeassemblydifferentialexpression
10 stars 5.78 score 5 scriptsbioc
R3CPET:3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process
The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results.
Maintained by Mohamed Nadhir Djekidel. Last updated 5 months ago.
networkinferencegenepredictionbayesiangraphandnetworknetworkgeneexpressionhicchia-petchromatin-interactiondirichlet-process-mixturestranscription-factocpp
4 stars 5.45 score 5 scriptsbioc
SurfR:Surface Protein Prediction and Identification
Identify Surface Protein coding genes from a list of candidates. Systematically download data from GEO and TCGA or use your own data. Perform DGE on bulk RNAseq data. Perform Meta-analysis. Descriptive enrichment analysis and plots.
Maintained by Aurora Maurizio. Last updated 15 days ago.
softwaresequencingrnaseqgeneexpressiontranscriptiondifferentialexpressionprincipalcomponentgenesetenrichmentpathwaysbatcheffectfunctionalgenomicsvisualizationdataimportfunctionalpredictiongenepredictiongodgeenrichment-analysismetaanalysisplotsproteinspublic-datasurfacesurfaceome
3 stars 5.43 score 3 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
5.20 score 20 scriptsbioc
MouseFM:In-silico methods for genetic finemapping in inbred mice
This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%).
Maintained by Matthias Munz. Last updated 5 months ago.
geneticssnpgenetargetvariantannotationgenomicvariationmultiplecomparisonsystemsbiologymathematicalbiologypatternlogicgenepredictionbiomedicalinformaticsfunctionalgenomicsfinemapgene-candidatesinbred-miceinbred-strainsmouseqtlqtl-mapping
5.13 score 5 scriptsbioc
HPiP:Host-Pathogen Interaction Prediction
HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.
Maintained by Matineh Rahmatbakhsh. Last updated 5 months ago.
proteomicssystemsbiologynetworkinferencestructuralpredictiongenepredictionnetwork
3 stars 4.95 score 6 scriptsbioc
sigFeature:sigFeature: Significant feature selection using SVM-RFE & t-statistic
This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.
Maintained by Pijush Das Developer. Last updated 5 months ago.
featureextractiongeneexpressionmicroarraytranscriptionmrnamicroarraygenepredictionnormalizationclassificationsupportvectormachine
4.92 score 21 scriptsbioc
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
4.92 score 83 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
4.60 score 4 scriptsbioc
DeepTarget:Deep characterization of cancer drugs
This package predicts a drug’s primary target(s) or secondary target(s) by integrating large-scale genetic and drug screens from the Cancer Dependency Map project run by the Broad Institute. It further investigates whether the drug specifically targets the wild-type or mutated target forms. To show how to use this package in practice, we provided sample data along with step-by-step example.
Maintained by Trinh Nguyen. Last updated 5 months ago.
genetargetgenepredictionpathwaysgeneexpressionrnaseqimmunooncologydifferentialexpressiongenesetenrichmentreportwritingcrispr
4.54 score 1 scriptsbioc
RLassoCox:A reweighted Lasso-Cox by integrating gene interaction information
RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.
Maintained by Wei Liu. Last updated 5 months ago.
survivalregressiongeneexpressiongenepredictionnetwork
3 stars 4.48 score 2 scriptsbioc
PDATK:Pancreatic Ductal Adenocarcinoma Tool-Kit
Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making.
Maintained by Benjamin Haibe-Kains. Last updated 5 months ago.
geneexpressionpharmacogeneticspharmacogenomicssoftwareclassificationsurvivalclusteringgeneprediction
1 stars 4.31 score 17 scriptsbioc
AssessORF:Assess Gene Predictions Using Proteomics and Evolutionary Conservation
In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence.
Maintained by Deepank Korandla. Last updated 5 months ago.
comparativegenomicsgenepredictiongenomeannotationgeneticsproteomicsqualitycontrolvisualization
4.18 score 3 scriptsbioc
pram:Pooling RNA-seq datasets for assembling transcript models
Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models.
Maintained by Peng Liu. Last updated 5 months ago.
softwaretechnologysequencingrnaseqbiologicalquestiongenepredictiongenomeannotationresearchfieldtranscriptomicsbioconductor-packagegenome-annotationrna-seqtranscript-model
1 stars 4.18 score 3 scriptsbioc
factR:Functional Annotation of Custom Transcriptomes
factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts.
Maintained by Fursham Hamid. Last updated 5 months ago.
alternativesplicingfunctionalpredictiongenepredictioncustom-transcriptomesfunctional-annotationgtfrna-seq-analysis
1 stars 4.00 score 5 scriptsbioc
PhenoGeneRanker:PhenoGeneRanker: A gene and phenotype prioritization tool
This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155.
Maintained by Cagatay Dursun. Last updated 5 months ago.
biomedicalinformaticsgenepredictiongraphandnetworknetworknetworkinferencepathwayssoftwaresystemsbiology
4.00 score 1 scriptsbioc
geneAttribution:Identification of candidate genes associated with genetic variation
Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format.
Maintained by Arthur Wuster. Last updated 5 months ago.
snpgenepredictiongenomewideassociationvariantannotationgenomicvariation
4.00 score 3 scriptsbioc
ZygosityPredictor:Package for prediction of zygosity for variants/genes in NGS data
The ZygosityPredictor allows to predict how many copies of a gene are affected by small variants. In addition to the basic calculations of the affected copy number of a variant, the Zygosity-Predictor can integrate the influence of several variants on a gene and ultimately make a statement if and how many wild-type copies of the gene are left. This information proves to be of particular use in the context of translational medicine. For example, in cancer genomes, the Zygosity-Predictor can address whether unmutated copies of tumor-suppressor genes are present. Beyond this, it is possible to make this statement for all genes of an organism. The Zygosity-Predictor was primarily developed to handle SNVs and INDELs (later addressed as small-variants) of somatic and germline origin. In order not to overlook severe effects outside of the small-variant context, it has been extended with the assessment of large scale deletions, which cause losses of whole genes or parts of them.
Maintained by Marco Rheinnecker. Last updated 5 months ago.
biomedicalinformaticsfunctionalpredictionsomaticmutationgeneprediction
3.85 score 2 scriptsbioc
pfamAnalyzeR:Identification of domain isotypes in pfam data
Protein domains is one of the most import annoation of proteins we have with the Pfam database/tool being (by far) the most used tool. This R package enables the user to read the pfam prediction from both webserver and stand-alone runs into R. We have recently shown most human protein domains exist as multiple distinct variants termed domain isotypes. Different domain isotypes are used in a cell, tissue, and disease-specific manner. Accordingly, we find that domain isotypes, compared to each other, modulate, or abolish the functionality of a protein domain. This R package enables the identification and classification of such domain isotypes from Pfam data.
Maintained by Kristoffer Vitting-Seerup. Last updated 5 months ago.
alternativesplicingtranscriptomevariantbiomedicalinformaticsfunctionalgenomicssystemsbiologyannotationfunctionalpredictiongenepredictiondataimport
1 stars 3.78 score 1 scripts 1 dependents
cran
binomialRF:Binomial Random Forest Feature Selection
The 'binomialRF' is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, 'binomialRF' then tests whether a feature is selected more often than by random chance.
Maintained by Samir Rachid Zaim. Last updated 5 years ago.
softwaregenepredictionstatisticalmethoddecisiontreedimensionreductionexperimentaldesign
2.70 score
fabriciomlopes
BASiNETEntropy:Classification of RNA Sequences using Complex Network and Information Theory
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of maximum entropy for the purpose of making a classification between the classes of the sequences. There are two data present in the 'BASiNET' package, "mRNA", and "ncRNA" with 10 sequences. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples.
Maintained by Fabricio Martins Lopes. Last updated 2 years ago.
softwarebiologicalquestiongenepredictionfunctionalpredictionnetworkclassification
2.70 score 6 scriptsfabriciomlopes
BASiNET:Classification of RNA Sequences using Complex Network Theory
It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.
Maintained by Fabricio Martins Lopes. Last updated 3 years ago.
softwarebiologicalquestiongenepredictionopenjdk
2.48 score 7 scripts