R-universe search: topic:generegulation

bioc

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 1 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network sequencing survival software bioc bioconductor gdc integrative-analysis tcga tcga-data tcgabiolinks

310 stars 14.47 score 1.6k scripts 6 dependents

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TFBSTools:Software Package for Transcription Factor Binding Site (TFBS) Analysis

TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software.

Maintained by Ge Tan. Last updated 17 days ago.

motifannotation generegulation motifdiscovery transcription alignment

28 stars 12.36 score 1.1k scripts 18 dependents

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decoupleR:decoupleR: Ensemble of computational methods to infer biological activities from omics data

Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase.

Maintained by Pau Badia-i-Mompel. Last updated 5 months ago.

differentialexpression functionalgenomics geneexpression generegulation network software statisticalmethod transcription

230 stars 11.27 score 316 scripts 3 dependents

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universalmotif:Import, Modify, and Export Motifs with R

Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.

Maintained by Benjamin Jean-Marie Tremblay. Last updated 5 months ago.

motifannotation motifdiscovery dataimport generegulation motif-analysis motif-enrichment-analysis sequence-logo cpp

28 stars 11.04 score 342 scripts 12 dependents

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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 1 months ago.

graphandnetwork network pathways software thirdpartyclient dataimport datarepresentation genesignaling generegulation systemsbiology transcriptomics singlecell annotation kegg complexes enzyme-ptm networks networks-biology omnipath proteins quarto

130 stars 9.90 score 226 scripts 2 dependents

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RcisTarget:RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions

RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge).

Maintained by Gert Hulselmans. Last updated 5 months ago.

generegulation motifannotation transcriptomics transcription genesetenrichment genetarget

37 stars 9.47 score 191 scripts

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LOLA:Locus overlap analysis for enrichment of genomic ranges

Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data.

Maintained by Nathan Sheffield. Last updated 5 months ago.

genesetenrichment generegulation genomeannotation systemsbiology functionalgenomics chipseq methylseq sequencing

76 stars 9.34 score 160 scripts

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CNEr:CNE Detection and Visualization

Large-scale identification and advanced visualization of sets of conserved noncoding elements.

Maintained by Ge Tan. Last updated 5 months ago.

generegulation visualization dataimport

3 stars 9.28 score 35 scripts 19 dependents

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Rsubread:Mapping, quantification and variant analysis of sequencing data

Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads.

Maintained by Wei Shi. Last updated 9 days ago.

sequencing alignment sequencematching rnaseq chipseq singlecell geneexpression generegulation genetics immunooncology snp geneticvariability preprocessing qualitycontrol genomeannotation genefusiondetection indeldetection variantannotation variantdetection multiplesequencealignment zlib

9.24 score 892 scripts 10 dependents

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progeny:Pathway RespOnsive GENes for activity inference from gene expression

PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics.

Maintained by Aurélien Dugourd. Last updated 5 months ago.

systemsbiology geneexpression functionalprediction generegulation

99 stars 8.90 score 221 scripts 1 dependents

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qpgraph:Estimation of Genetic and Molecular Regulatory Networks from High-Throughput Genomics Data

Estimate gene and eQTL networks from high-throughput expression and genotyping assays.

Maintained by Robert Castelo. Last updated 1 days ago.

microarray geneexpression transcription pathways networkinference graphandnetwork generegulation genetics geneticvariability snp software openblas

3 stars 8.72 score 20 scripts 3 dependents

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memes:motif matching, comparison, and de novo discovery using the MEME Suite

A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows.

Maintained by Spencer Nystrom. Last updated 5 months ago.

dataimport functionalgenomics generegulation motifannotation motifdiscovery sequencematching software

50 stars 8.69 score 117 scripts 1 dependents

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projectR:Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering

Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.

Maintained by Genevieve Stein-OBrien. Last updated 11 days ago.

functionalprediction generegulation biologicalquestion software

62 stars 8.42 score 70 scripts

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nullranges:Generation of null ranges via bootstrapping or covariate matching

Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.

Maintained by Michael Love. Last updated 5 months ago.

visualization genesetenrichment functionalgenomics epigenetics generegulation genetarget genomeannotation annotation genomewideassociation histonemodification chipseq atacseq dnaseseq rnaseq hiddenmarkovmodel bioconductor bootstrap genomics matching statistics

27 stars 8.16 score 50 scripts 1 dependents

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dreamlet:Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Recent advances in single cell/nucleus transcriptomic technology has enabled collection of cohort-scale datasets to study cell type specific gene expression differences associated disease state, stimulus, and genetic regulation. The scale of these data, complex study designs, and low read count per cell mean that characterizing cell type specific molecular mechanisms requires a user-frieldly, purpose-build analytical framework. We have developed the dreamlet package that applies a pseudobulk approach and fits a regression model for each gene and cell cluster to test differential expression across individuals associated with a trait of interest. Use of precision-weighted linear mixed models enables accounting for repeated measures study designs, high dimensional batch effects, and varying sequencing depth or observed cells per biosample.

Maintained by Gabriel Hoffman. Last updated 4 days ago.

rnaseq geneexpression differentialexpression batcheffect qualitycontrol regression genesetenrichment generegulation epigenetics functionalgenomics transcriptomics normalization singlecell preprocessing sequencing immunooncology software cpp

12 stars 8.14 score 128 scripts

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netZooR:Unified methods for the inference and analysis of gene regulatory networks

netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information.

Maintained by Tara Eicher. Last updated 11 days ago.

networkinference network generegulation geneexpression transcription microarray graphandnetwork gene-regulatory-network transcription-factors

105 stars 7.98 score

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BioNERO:Biological Network Reconstruction Omnibus

BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.

Maintained by Fabricio Almeida-Silva. Last updated 5 months ago.

software geneexpression generegulation systemsbiology graphandnetwork preprocessing network networkinference

27 stars 7.69 score 50 scripts 1 dependents

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MIRA:Methylation-Based Inference of Regulatory Activity

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.

Maintained by John Lawson. Last updated 5 months ago.

immunooncology dnamethylation generegulation genomeannotation systemsbiology functionalgenomics chipseq methylseq sequencing epigenetics coverage

12 stars 7.56 score 7 scripts 1 dependents

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eisaR:Exon-Intron Split Analysis (EISA) in R

Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R.

Maintained by Michael Stadler. Last updated 3 months ago.

transcription geneexpression generegulation functionalgenomics transcriptomics regression rnaseq

16 stars 7.48 score 63 scripts

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CAGEfightR:Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor

CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.

Maintained by Malte Thodberg. Last updated 5 months ago.

software transcription coverage geneexpression generegulation peakdetection dataimport datarepresentation transcriptomics sequencing annotation genomebrowsers normalization preprocessing visualization

8 stars 7.46 score 67 scripts 1 dependents

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ELMER:Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes

ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue.

Maintained by Tiago Chedraoui Silva. Last updated 5 months ago.

dnamethylation geneexpression motifannotation software generegulation transcription network

7.42 score 176 scripts

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chromVAR:Chromatin Variation Across Regions

Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments.

Maintained by Alicia Schep. Last updated 5 months ago.

singlecell sequencing generegulation immunooncology cpp

7.31 score 772 scripts

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CRISPRseek:Design of guide RNAs in CRISPR genome-editing systems

The package encompasses functions to find potential guide RNAs for the CRISPR-based genome-editing systems including the Base Editors and the Prime Editors when supplied with target sequences as input. Users have the flexibility to filter resulting guide RNAs based on parameters such as the absence of restriction enzyme cut sites or the lack of paired guide RNAs. The package also facilitates genome-wide exploration for off-targets, offering features to score and rank off-targets, retrieve flanking sequences, and indicate whether the hits are located within exon regions. All detected guide RNAs are annotated with the cumulative scores of the top5 and topN off-targets together with the detailed information such as mismatch sites and restrictuion enzyme cut sites. The package also outputs INDELs and their frequencies for Cas9 targeted sites.

Maintained by Lihua Julie Zhu. Last updated 19 days ago.

immunooncology generegulation sequencematching crispr

7.18 score 51 scripts 2 dependents

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DiffBind:Differential Binding Analysis of ChIP-Seq Peak Data

Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions.

Maintained by Rory Stark. Last updated 2 months ago.

sequencing chipseq atacseq dnaseseq methylseq ripseq differentialpeakcalling differentialmethylation generegulation histonemodification peakdetection biomedicalinformatics cellbiology multiplecomparison normalization reportwriting epigenetics functionalgenomics curl bzip2 xz-utils zlib cpp

7.13 score 512 scripts 2 dependents

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ATACseqQC:ATAC-seq Quality Control

ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints.

Maintained by Jianhong Ou. Last updated 3 months ago.

sequencing dnaseq atacseq generegulation qualitycontrol coverage nucleosomepositioning immunooncology

7.12 score 146 scripts 1 dependents

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COCOA:Coordinate Covariation Analysis

COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data.

Maintained by John Lawson. Last updated 5 months ago.

epigenetics dnamethylation atacseq dnaseseq methylseq methylationarray principalcomponent genomicvariation generegulation genomeannotation systemsbiology functionalgenomics chipseq sequencing immunooncology dna-methylation pca

10 stars 7.02 score 21 scripts

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viper:Virtual Inference of Protein-activity by Enriched Regulon analysis

Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms

Maintained by Mariano J Alvarez. Last updated 5 months ago.

systemsbiology networkenrichment geneexpression functionalprediction generegulation

7.00 score 342 scripts 5 dependents

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epiregulon:Gene regulatory network inference from single cell epigenomic data

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 20 days ago.

singlecell generegulation networkinference network geneexpression transcription genetarget cpp

14 stars 6.67 score 17 scripts

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proActiv:Estimate Promoter Activity from RNA-Seq data

Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions.

Maintained by Joseph Lee. Last updated 5 months ago.

rnaseq geneexpression transcription alternativesplicing generegulation differentialsplicing functionalgenomics epigenetics transcriptomics preprocessing alternative-promoters genomics promoter-activity promoter-annotation rna-seq-data

51 stars 6.66 score 15 scripts

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MoonlightR:Identify oncogenes and tumor suppressor genes from omics data

Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments.

Maintained by Matteo Tiberti. Last updated 5 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

17 stars 6.57 score

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Moonlight2R:Identify oncogenes and tumor suppressor genes from omics data

The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). We present an updated version of the R/bioconductor package called MoonlightR, namely Moonlight2R, which returns a list of candidate driver genes for specific cancer types on the basis of omics data integration. The Moonlight framework contains a primary layer where gene expression data and information about biological processes are integrated to predict genes called oncogenic mediators, divided into putative tumor suppressors and putative oncogenes. This is done through functional enrichment analyses, gene regulatory networks and upstream regulator analyses to score the importance of well-known biological processes with respect to the studied cancer type. By evaluating the effect of the oncogenic mediators on biological processes or through random forests, the primary layer predicts two putative roles for the oncogenic mediators: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer through new functionalities in Moonlight2R. These functionalities analyze mutations in the cancer cohort and classifies these into driver and passenger mutations using the driver mutation prediction tool, CScape-somatic. Those oncogenic mediators with at least one driver mutation are retained as the driver genes. As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, Moonlight2R can be used to discover OCGs and TSGs in the same cancer type. This may for instance help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV). In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. An additional mechanistic layer evaluates if there are mutations affecting the protein stability of the transcription factors (TFs) of the TSGs and OCGs, as that may have an effect on the expression of the genes.

Maintained by Matteo Tiberti. Last updated 2 months ago.

dnamethylation differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network survival genesetenrichment networkenrichment

5 stars 6.41 score 43 scripts

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sRACIPE:Systems biology tool to simulate gene regulatory circuits

sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation.

Maintained by Mingyang Lu. Last updated 1 months ago.

researchfield systemsbiology mathematicalbiology geneexpression generegulation genetarget cpp

4 stars 6.40 score 209 scripts

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mpra:Analyze massively parallel reporter assays

Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA).

Maintained by Leslie Myint. Last updated 5 months ago.

software generegulation sequencing functionalgenomics

6 stars 6.28 score 15 scripts

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scMET:Bayesian modelling of cell-to-cell DNA methylation heterogeneity

High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression.

Maintained by Andreas C. Kapourani. Last updated 5 months ago.

immunooncology dnamethylation differentialmethylation differentialexpression geneexpression generegulation epigenetics genetics clustering featureextraction regression bayesian sequencing coverage singlecell bayesian-inference generalised-linear-models heterogeneity hierarchical-models methylation-analysis single-cell cpp

20 stars 6.23 score 42 scripts

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APAlyzer:A toolkit for APA analysis using RNA-seq data

Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data.

Maintained by Ruijia Wang. Last updated 5 months ago.

sequencing rnaseq differentialexpression geneexpression generegulation annotation dataimport software ative-polyadenylation bioinformatics-tool rna-seq

9 stars 5.86 score 9 scripts

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RTN:RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons

A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons.

Maintained by Mauro Castro. Last updated 5 months ago.

transcription network networkinference networkenrichment generegulation geneexpression graphandnetwork genesetenrichment geneticvariability

5.80 score 53 scripts 2 dependents

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BindingSiteFinder:Binding site defintion based on iCLIP data

Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette.

Maintained by Mirko Brüggemann. Last updated 10 days ago.

sequencing geneexpression generegulation functionalgenomics coverage dataimport binding-site-classification binding-sites bioconductor-package iclip rna-binding-proteins

6 stars 5.80 score 3 scripts

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cicero:Predict cis-co-accessibility from single-cell chromatin accessibility data

Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data.

Maintained by Hannah Pliner. Last updated 5 months ago.

sequencing clustering cellbasedassays immunooncology generegulation genetarget epigenetics atacseq singlecell

5.80 score 312 scripts

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BPRMeth:Model higher-order methylation profiles

The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles.

Maintained by Chantriolnt-Andreas Kapourani. Last updated 5 months ago.

immunooncology dnamethylation geneexpression generegulation epigenetics genetics clustering featureextraction regression rnaseq bayesian kegg sequencing coverage singlecell openblas cpp

5.75 score 94 scripts 1 dependents

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GDCRNATools:GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage.

Maintained by Ruidong Li. Last updated 5 months ago.

immunooncology geneexpression differentialexpression generegulation genetarget networkinference survival visualization genesetenrichment networkenrichment network rnaseq go kegg

5.64 score 44 scripts

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RIVER:R package for RIVER (RNA-Informed Variant Effect on Regulation)

An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples.

Maintained by Yungil Kim. Last updated 5 months ago.

geneexpression geneticvariability snp transcription functionalprediction generegulation genomicvariation biomedicalinformatics functionalgenomics genetics systemsbiology transcriptomics bayesian clustering transcriptomevariant regression functional-variants variant

11 stars 5.52 score 5 scripts

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miRSM:Inferring miRNA sponge modules in heterogeneous data

The package aims to identify miRNA sponge or ceRNA modules in heterogeneous data. It provides several functions to study miRNA sponge modules at single-sample and multi-sample levels, including popular methods for inferring gene modules (candidate miRNA sponge or ceRNA modules), and two functions to identify miRNA sponge modules at single-sample and multi-sample levels, as well as several functions to conduct modular analysis of miRNA sponge modules.

Maintained by Junpeng Zhang. Last updated 5 months ago.

geneexpression biomedicalinformatics clustering genesetenrichment microarray software generegulation genetarget cerna mirna mirna-sponge mirna-targets modules openjdk

4 stars 5.51 score 5 scripts

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GRaNIE:GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using single-cell or bulk chromatin accessibility and RNA-seq data

Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have cell-type specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using single-cell or bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally (Capture) Hi-C data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach.

Maintained by Christian Arnold. Last updated 5 months ago.

software geneexpression generegulation networkinference genesetenrichment biomedicalinformatics genetics transcriptomics atacseq rnaseq graphandnetwork regression transcription chipseq

5.40 score 24 scripts

bioc

SPONGE:Sparse Partial Correlations On Gene Expression

This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

Maintained by Markus List. Last updated 5 months ago.

geneexpression transcription generegulation networkinference transcriptomics systemsbiology regression randomforest machinelearning

5.36 score 38 scripts 1 dependents

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DEWSeq:Differential Expressed Windows Based on Negative Binomial Distribution

DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data.

Maintained by bioinformatics team Hentze. Last updated 5 months ago.

sequencing generegulation functionalgenomics differentialexpression bioinformatics eclip ngs-analysis

5 stars 5.30 score 4 scripts

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DegCre:Probabilistic association of DEGs to CREs from differential data

DegCre generates associations between differentially expressed genes (DEGs) and cis-regulatory elements (CREs) based on non-parametric concordance between differential data. The user provides GRanges of DEG TSS and CRE regions with differential p-value and optionally log-fold changes and DegCre returns an annotated Hits object with associations and their calculated probabilities. Additionally, the package provides functionality for visualization and conversion to other formats.

Maintained by Brian S. Roberts. Last updated 4 months ago.

geneexpression generegulation atacseq chipseq dnaseseq rnaseq

5 stars 5.30 score 2 scripts

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regutools:regutools: an R package for data extraction from RegulonDB

RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks.

Maintained by Joselyn Chavez. Last updated 4 months ago.

generegulation geneexpression systemsbiology network networkinference visualization transcription bioconductor cdsb regulondb

4 stars 5.20 score 6 scripts

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FindIT2:find influential TF and Target based on multi-omics data

This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF.

Maintained by Guandong Shang. Last updated 5 months ago.

software annotation chipseq atacseq generegulation multiplecomparison genetarget

6 stars 5.08 score 7 scripts

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epiregulon.extra:Companion package to epiregulon with additional plotting, differential and graph functions

Gene regulatory networks model the underlying gene regulation hierarchies that drive gene expression and observed phenotypes. Epiregulon infers TF activity in single cells by constructing a gene regulatory network (regulons). This is achieved through integration of scATAC-seq and scRNA-seq data and incorporation of public bulk TF ChIP-seq data. Links between regulatory elements and their target genes are established by computing correlations between chromatin accessibility and gene expressions.

Maintained by Xiaosai Yao. Last updated 10 days ago.

generegulation network geneexpression transcription chiponchip differentialexpression genetarget normalization graphandnetwork

4.95 score 10 scripts

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Melissa:Bayesian clustering and imputationa of single cell methylomes

Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach.

Maintained by C. A. Kapourani. Last updated 5 months ago.

immunooncology dnamethylation geneexpression generegulation epigenetics genetics clustering featureextraction regression rnaseq bayesian kegg sequencing coverage singlecell

4.90 score 7 scripts

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ribosomeProfilingQC:Ribosome Profiling Quality Control

Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis.

Maintained by Jianhong Ou. Last updated 2 months ago.

riboseq sequencing generegulation qualitycontrol visualization coverage

4.88 score 17 scripts

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AMARETTO:Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression

Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways.

Maintained by Olivier Gevaert. Last updated 5 months ago.

statisticalmethod differentialmethylation generegulation geneexpression methylationarray transcription preprocessing batcheffect dataimport mrnamicroarray micrornaarray regression clustering rnaseq copynumbervariation sequencing microarray normalization network bayesian exonarray onechannel twochannel proprietaryplatforms alternativesplicing differentialexpression differentialsplicing genesetenrichment multiplecomparison qualitycontrol timecourse

4.88 score 15 scripts

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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.

transcription network generegulation graphandnetwork bioconductor bioinformatics

3 stars 4.86 score 3 scripts 1 dependents

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IMAS:Integrative analysis of Multi-omics data for Alternative Splicing

Integrative analysis of Multi-omics data for Alternative splicing.

Maintained by Seonggyun Han. Last updated 5 months ago.

immunooncology alternativesplicing differentialexpression differentialsplicing geneexpression generegulation regression rnaseq sequencing snp software transcription

4.85 score 1 scripts

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airpart:Differential cell-type-specific allelic imbalance

Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.

Maintained by Wancen Mu. Last updated 5 months ago.

singlecell rnaseq atacseq chipseq sequencing generegulation geneexpression transcription transcriptomevariant cellbiology functionalgenomics differentialexpression graphandnetwork regression clustering qualitycontrol

2 stars 4.78 score 2 scripts

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sarks:Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains

Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.

Maintained by Dennis Wylie. Last updated 5 months ago.

motifdiscovery generegulation geneexpression transcriptomics rnaseq differentialexpression featureextraction openjdk

3 stars 4.78 score 3 scripts

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IVAS:Identification of genetic Variants affecting Alternative Splicing

Identification of genetic variants affecting alternative splicing.

Maintained by Seonggyun Han. Last updated 5 months ago.

immunooncology alternativesplicing differentialexpression differentialsplicing geneexpression generegulation regression rnaseq sequencing snp software transcription

4.78 score 1 scripts 1 dependents

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MethylMix:MethylMix: Identifying methylation driven cancer genes

MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8.

Maintained by Olivier Gevaert. Last updated 5 months ago.

dnamethylation statisticalmethod differentialmethylation generegulation geneexpression methylationarray differentialexpression pathways network

4.72 score 26 scripts

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rigvf:R interface to the IGVF Catalog

The IGVF Catalog provides data on the impact of genomic variants on function. The `rigvf` package provides an interface to the IGVF Catalog, allowing easy integration with Bioconductor resources.

Maintained by Michael Love. Last updated 10 days ago.

thirdpartyclient annotation variantannotation functionalgenomics generegulation genomicvariation genetarget

4.70 score

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epidecodeR:epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation

epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes.

Maintained by Kandarp Joshi. Last updated 5 months ago.

differentialexpression generegulation histonemodification functionalprediction transcription geneexpression epitranscriptomics epigenetics functionalgenomics systemsbiology transcriptomics chiponchip differential-expression genomics genomics-visualization

5 stars 4.70 score 1 scripts

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rifi:'rifi' analyses data from rifampicin time series created by microarray or RNAseq

'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced.

Maintained by Jens Georg. Last updated 5 months ago.

rnaseq differentialexpression generegulation transcriptomics regression microarray software

4.60 score 1 scripts

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KBoost:Inference of gene regulatory networks from gene expression data

Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets.

Maintained by Luis F. Iglesias-Martinez. Last updated 5 months ago.

network graphandnetwork bayesian networkinference generegulation transcriptomics systemsbiology transcription geneexpression regression principalcomponent

4 stars 4.60 score 9 scripts

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bnem:Training of logical models from indirect measurements of perturbation experiments

bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate).

Maintained by Martin Pirkl. Last updated 5 months ago.

pathways systemsbiology networkinference network geneexpression generegulation preprocessing

2 stars 4.60 score 5 scripts

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BCRANK:Predicting binding site consensus from ranked DNA sequences

Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search

Maintained by Adam Ameur. Last updated 5 months ago.

motifdiscovery generegulation

4.49 score 26 scripts

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seqArchR:Identify Different Architectures of Sequence Elements

seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos.

Maintained by Sarvesh Nikumbh. Last updated 5 months ago.

motifdiscovery generegulation mathematicalbiology systemsbiology transcriptomics genetics clustering dimensionreduction featureextraction dnaseq nmf nonnegative-matrix-factorization promoter-sequence-architectures scikit-learn sequence-analysis sequence-architectures unsupervised-machine-learning

1 stars 4.48 score 9 scripts 1 dependents

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primirTSS:Prediction of pri-miRNA Transcription Start Site

A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.

Maintained by Pumin Li. Last updated 5 months ago.

immunooncology sequencing rnaseq genetics preprocessing transcription generegulation

4.48 score 2 scripts

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pqsfinder:Identification of potential quadruplex forming sequences

Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4.

Maintained by Jiri Hon. Last updated 5 months ago.

motifdiscovery sequencematching generegulation cpp

4.41 score 16 scripts

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tigre:Transcription factor Inference through Gaussian process Reconstruction of Expression

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

Maintained by Antti Honkela. Last updated 5 months ago.

microarray timecourse geneexpression transcription generegulation networkinference bayesian

4.38 score 6 scripts

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INSPEcT:Modeling RNA synthesis, processing and degradation with RNA-seq data

INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data.

Maintained by Stefano de Pretis. Last updated 5 months ago.

sequencing rnaseq generegulation timecourse systemsbiology

4.38 score 9 scripts

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omicRexposome:Exposome and omic data associatin and integration analysis

omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA).

Maintained by Xavier Escribà Montagut. Last updated 5 months ago.

immunooncology workflowstep multiplecomparison visualization geneexpression differentialexpression differentialmethylation generegulation epigenetics proteomics transcriptomics statisticalmethod regression

4.30 score 5 scripts

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RiboDiPA:Differential pattern analysis for Ribo-seq data

This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.

Maintained by Ji-Ping Wang. Last updated 4 months ago.

riboseq geneexpression generegulation differentialexpression sequencing coverage alignment rnaseq immunooncology qualitycontrol dataimport software normalization cpp

4.30 score 1 scripts

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spatzie:Identification of enriched motif pairs from chromatin interaction data

Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments.

Maintained by Jennifer Hammelman. Last updated 5 months ago.

dna3dstructure generegulation peakdetection epigenetics functionalgenomics classification hic transcription

4.30 score 5 scripts

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SELEX:Functions for analyzing SELEX-seq data

Tools for quantifying DNA binding specificities based on SELEX-seq data.

Maintained by Harmen J. Bussemaker. Last updated 5 months ago.

software motifdiscovery motifannotation generegulation transcription openjdk

4.30 score 8 scripts

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idr2d:Irreproducible Discovery Rate for Genomic Interactions Data

A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks.

Maintained by Konstantin Krismer. Last updated 5 months ago.

dna3dstructure generegulation peakdetection epigenetics functionalgenomics classification hic

4.30 score 6 scripts

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GIGSEA:Genotype Imputed Gene Set Enrichment Analysis

We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.

Maintained by Shijia Zhu. Last updated 5 months ago.

genesetenrichment snp variantannotation geneexpression generegulation regression differentialexpression

4.30 score 2 scripts

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anota2seq:Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq

anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks.

Maintained by Christian Oertlin. Last updated 5 months ago.

immunooncology geneexpression differentialexpression microarray genomewideassociation batcheffect normalization rnaseq sequencing generegulation regression

4.28 score 12 scripts

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GUIDEseq:GUIDE-seq and PEtag-seq analysis pipeline

The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites with mismatches and indels.

Maintained by Lihua Julie Zhu. Last updated 5 months ago.

immunooncology generegulation sequencing workflowstep crispr

4.23 score 14 scripts

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GRENITS:Gene Regulatory Network Inference Using Time Series

The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model.

Maintained by Edward Morrissey. Last updated 5 months ago.

networkinference generegulation timecourse graphandnetwork geneexpression network bayesian openblas cpp

4.20 score 2 scripts

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MIRit:Integrate microRNA and gene expression to decipher pathway complexity

MIRit is an R package that provides several methods for investigating the relationships between miRNAs and genes in different biological conditions. In particular, MIRit allows to explore the functions of dysregulated miRNAs, and makes it possible to identify miRNA-gene regulatory axes that control biological pathways, thus enabling the users to unveil the complexity of miRNA biology. MIRit is an all-in-one framework that aims to help researchers in all the central aspects of an integrative miRNA-mRNA analyses, from differential expression analysis to network characterization.

Maintained by Jacopo Ronchi. Last updated 10 days ago.

software generegulation networkenrichment networkinference epigenetics functionalgenomics systemsbiology network pathways geneexpression differentialexpression mirna mirna-mrna-interaction mirna-seq mirnaseq-analysis cpp

1 stars 4.18 score 2 scripts

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ATACseqTFEA:Transcription Factor Enrichment Analysis for ATAC-seq

Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions.

Maintained by Jianhong Ou. Last updated 3 months ago.

sequencing dnaseq atacseq mnaseseq generegulation

1 stars 4.18 score 4 scripts

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RNAdecay:Maximum Likelihood Decay Modeling of RNA Degradation Data

RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.

Maintained by Reed Sorenson. Last updated 5 months ago.

immunooncology software geneexpression generegulation differentialexpression transcription transcriptomics timecourse regression rnaseq normalization workflowstep

4.18 score 2 scripts

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NADfinder:Call wide peaks for sequencing data

Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation.

Maintained by Jianhong Ou. Last updated 3 months ago.

sequencing dnaseq generegulation peakdetection

4.18 score 1 scripts

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BUMHMM:Computational pipeline for computing probability of modification from structure probing experiment data

This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.

Maintained by Alina Selega. Last updated 5 months ago.

immunooncology geneticvariability transcription geneexpression generegulation coverage genetics structuralprediction transcriptomics bayesian classification featureextraction hiddenmarkovmodel regression rnaseq sequencing

4.15 score 14 scripts

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geneplast:Evolutionary and plasticity analysis of orthologous groups

Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution.

Maintained by Mauro Castro. Last updated 5 months ago.

genetics generegulation systemsbiology

4.13 score 17 scripts

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rifiComparative:'rifiComparative' compares the output of rifi from two different conditions.

'rifiComparative' is a continuation of rifi package. It compares two conditions output of rifi using half-life and mRNA at time 0 segments. As an input for the segmentation, the difference between half-life of both condtions and log2FC of the mRNA at time 0 are used. The package provides segmentation, statistics, summary table, fragments visualization and some additional useful plots for further anaylsis.

Maintained by Loubna Youssar. Last updated 5 months ago.

rnaseq differentialexpression generegulation transcriptomics microarray software

4.00 score

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GenomicOZone:Delineate outstanding genomic zones of differential gene activity

The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data.

Maintained by Hua Zhong. Last updated 5 months ago.

software geneexpression transcription differentialexpression functionalprediction generegulation biomedicalinformatics cellbiology functionalgenomics genetics systemsbiology transcriptomics clustering regression rnaseq annotation visualization sequencing coverage differentialmethylation genomicvariation structuralvariation copynumbervariation

4.00 score

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AffiXcan:A Functional Approach To Impute Genetically Regulated Expression

Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known.

Maintained by Alessandro Lussana. Last updated 5 months ago.

geneexpression transcription generegulation dimensionreduction regression principalcomponent

4.00 score

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xcore:xcore expression regulators inference

xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes.

Maintained by Maciej Migdał. Last updated 5 months ago.

geneexpression generegulation epigenetics regression sequencing

4.00 score 8 scripts

bioc

BiFET:Bias-free Footprint Enrichment Test

BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis.

Maintained by Ahrim Youn. Last updated 5 months ago.

immunooncology genetics epigenetics transcription generegulation atacseq dnaseseq ripseq software

4.00 score 4 scripts

bioc

Cogito:Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets

Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis.

Maintained by Annika Bürger. Last updated 5 months ago.

functionalgenomics generegulation software sequencing

4.00 score 3 scripts

bioc

GSReg:Gene Set Regulation (GS-Reg)

A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA).

Maintained by Bahman Afsari. Last updated 5 months ago.

generegulation pathways geneexpression geneticvariability genesetenrichment alternativesplicing

3.98 score 16 scripts

bioc

terapadog:Translational Efficiency Regulation Analysis using the PADOG Method

This package performs a Gene Set Analysis with the approach adopted by PADOG on the genes that are reported as translationally regulated (ie. exhibit a significant change in TE) by the DeltaTE package. It can be used on its own to see the impact of translation regulation on gene sets, but it is also integrated as an additional analysis method within ReactomeGSA, where results are further contextualised in terms of pathways and directionality of the change.

Maintained by Gionmattia Carancini. Last updated 24 days ago.

riboseq transcriptomics genesetenrichment generegulation reactome software

3.90 score

bioc

pandaR:PANDA Algorithm

Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources.

Maintained by Joseph N. Paulson. Last updated 5 months ago.

statisticalmethod graphandnetwork microarray generegulation networkinference geneexpression transcription network

3.78 score 8 scripts 1 dependents

bioc

PIPETS:Poisson Identification of PEaks from Term-Seq data

PIPETS provides statistically robust analysis for 3'-seq/term-seq data. It utilizes a sliding window approach to apply a Poisson Distribution test to identify genomic positions with termination read coverage that is significantly higher than the surrounding signal. PIPETS then condenses proximal signal and produces strand specific results that contain all significant termination peaks.

Maintained by Quinlan Furumo. Last updated 5 months ago.

sequencing transcription generegulation peakdetection genetics transcriptomics coverage

3.78 score 2 scripts

bioc

RTNduals:Analysis of co-regulation and inference of 'dual regulons'

RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets.

Maintained by Mauro Castro. Last updated 5 months ago.

generegulation geneexpression networkenrichment networkinference graphandnetwork

3.78 score 2 scripts 1 dependents

bioc

TFEA.ChIP:Analyze Transcription Factor Enrichment

Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments.

Maintained by Laura Puente Santamaría. Last updated 5 months ago.

transcription generegulation genesetenrichment transcriptomics sequencing chipseq rnaseq immunooncology

3.45 score 14 scripts

bioc

GSRI:Gene Set Regulation Index

The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI).

Maintained by Julian Gehring. Last updated 5 months ago.

microarray transcription differentialexpression genesetenrichment generegulation

3.30 score 2 scripts

bioc

blima:Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level

Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.

Maintained by Vojtěch Kulvait. Last updated 5 months ago.

microarray preprocessing normalization differentialexpression generegulation geneexpression cpp

3.30 score 1 scripts

bioc

RTNsurvival:Survival analysis using transcriptional networks inferred by the RTN package

RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications.

Maintained by Clarice Groeneveld. Last updated 5 months ago.

networkenrichment survival generegulation genesetenrichment networkinference graphandnetwork

3.30 score 9 scripts

bioc

profileScoreDist:Profile score distributions

Regularization and score distributions for position count matrices.

Maintained by Paal O. Westermark. Last updated 5 months ago.

software generegulation statisticalmethod cpp

3.30 score 1 scripts

bioc

rTRMui:A shiny user interface for rTRM

This package provides a web interface to compute transcriptional regulatory modules with rTRM.

Maintained by Diego Diez. Last updated 5 months ago.

transcription network generegulation graphandnetwork gui

1 stars 3.30 score 1 scripts

bioc

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.

cellbiology generegulation regulation geneexpression differentialexpression microarray highthroughputsequencing qualitycontrol go multiplecomparisons bioinformatics

3.30 score 2 scripts

bioc

BasicSTARRseq:Basic peak calling on STARR-seq data

Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17.

Maintained by Annika Buerger. Last updated 5 months ago.

peakdetection generegulation functionalprediction functionalgenomics coverage

3.30 score 1 scripts

bioc

MatrixRider:Obtain total affinity and occupancies for binding site matrices on a given sequence

Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar.

Maintained by Elena Grassi. Last updated 5 months ago.

generegulation genetics motifannotation

3.30 score 5 scripts

bioc

diggit:Inference of Genetic Variants Driving Cellular Phenotypes

Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm

Maintained by Mariano J Alvarez. Last updated 5 months ago.

systemsbiology networkenrichment geneexpression functionalprediction generegulation

3.30 score 3 scripts

bioc

triplex:Search and visualize intramolecular triplex-forming sequences in DNA

This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D.

Maintained by Jiri Hon. Last updated 5 months ago.

sequencematching generegulation

3.30 score 2 scripts

bioc

ISoLDE:Integrative Statistics of alleLe Dependent Expression

This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below.

Maintained by Christelle Reynès. Last updated 5 months ago.

immunooncology geneexpression transcription genesetenrichment genetics sequencing rnaseq multiplecomparison snp geneticvariability epigenetics mathematicalbiology generegulation openmp

2.30 score 2 scripts