R-universe search: needs:DESeq2

bioc

muscat:Multi-sample multi-group scRNA-seq data analysis tools

`muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.

Maintained by Helena L. Crowell. Last updated 5 months ago.

immunooncology differentialexpression sequencing singlecell software statisticalmethod visualization

184 stars 10.74 score 686 scripts 1 dependents

bioc

Glimma:Interactive visualizations for gene expression analysis

This package produces interactive visualizations for RNA-seq data analysis, utilizing output from limma, edgeR, or DESeq2. It produces interactive htmlwidgets versions of popular RNA-seq analysis plots to enhance the exploration of analysis results by overlaying interactive features. The plots can be viewed in a web browser or embedded in notebook documents.

Maintained by Shian Su. Last updated 2 months ago.

differentialexpression geneexpression microarray reportwriting rnaseq sequencing visualization differential-expression interactive-visualizations

32 stars 10.58 score 600 scripts 1 dependents

bioc

ORFik:Open Reading Frames in Genomics

R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more.

Maintained by Haakon Tjeldnes. Last updated 1 months ago.

immunooncology software sequencing riboseq rnaseq functionalgenomics coverage alignment dataimport cpp

33 stars 10.56 score 115 scripts 2 dependents

bioc

singleCellTK:Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk.

Maintained by Joshua David Campbell. Last updated 1 months ago.

singlecell geneexpression differentialexpression alignment clustering immunooncology batcheffect normalization qualitycontrol dataimport gui

182 stars 10.17 score 252 scripts

bioc

pcaExplorer:Interactive Visualization of RNA-seq Data Using a Principal Components Approach

This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology visualization rnaseq dimensionreduction principalcomponent qualitycontrol gui reportwriting shinyapps bioconductor principal-components reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

56 stars 9.63 score 180 scripts

bioc

DEGreport:Report of DEG analysis

Creation of ready-to-share figures of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene.

Maintained by Lorena Pantano. Last updated 5 months ago.

differentialexpression visualization rnaseq reportwriting geneexpression immunooncology bioconductor differential-expression qc report rna-seq smallrna

24 stars 9.42 score 354 scripts 1 dependents

bioc

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.

geneexpression transcription alternativesplicing differentialexpression differentialsplicing visualization statisticalmethod transcriptomevariant biomedicalinformatics functionalgenomics systemsbiology transcriptomics rnaseq annotation functionalprediction geneprediction dataimport multiplecomparison batcheffect immunooncology

108 stars 9.26 score 125 scripts

bioc

OUTRIDER:OUTRIDER - OUTlier in RNA-Seq fInDER

Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results.

Maintained by Christian Mertes. Last updated 5 months ago.

immunooncology rnaseq transcriptomics alignment sequencing geneexpression genetics count-data diagnostics expression-analysis mendelian-genetics outlier-detection rna-seq openblas cpp

50 stars 9.07 score 110 scripts 1 dependents

bioc

BatchQC:Batch Effects Quality Control Software

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

Maintained by Jessica Anderson. Last updated 12 days ago.

batcheffect graphandnetwork microarray normalization principalcomponent sequencing software visualization qualitycontrol rnaseq preprocessing differentialexpression immunooncology

7 stars 9.06 score 54 scripts

bioc

FRASER:Find RAre Splicing Events in RNA-Seq Data

Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects.

Maintained by Christian Mertes. Last updated 5 months ago.

rnaseq alternativesplicing sequencing software genetics coverage aberrant-splicing diagnostics outlier-detection rare-disease rna-seq splicing openblas cpp

41 stars 8.50 score 155 scripts

bioc

GeneTonic:Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis

This package provides functionality to combine the existing pieces of the transcriptome data and results, making it easier to generate insightful observations and hypothesis. Its usage is made easy with a Shiny application, combining the benefits of interactivity and reproducibility e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. Using the GeneTonicList as a standardized container for all the required components, it is possible to simplify the generation of multiple visualizations and summaries.

Maintained by Federico Marini. Last updated 3 months ago.

gui geneexpression software transcription transcriptomics visualization differentialexpression pathways reportwriting genesetenrichment annotation go shinyapps bioconductor bioconductor-package data-exploration data-visualization functional-enrichment-analysis gene-expression pathway-analysis reproducible-research rna-seq-analysis rna-seq-data shiny transcriptome user-friendly

77 stars 8.28 score 37 scripts 1 dependents

bioc

POMA:Tools for Omics Data Analysis

The POMA package offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness, POMA leverages the standardized SummarizedExperiment class from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, making POMA an essential asset for researchers handling omics datasets. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) <doi:10.1371/journal.pcbi.1009148> for more details.

Maintained by Pol Castellano-Escuder. Last updated 4 months ago.

batcheffect classification clustering decisiontree dimensionreduction multidimensionalscaling normalization preprocessing principalcomponent regression rnaseq software statisticalmethod visualization bioconductor bioinformatics data-visualization dimension-reduction exploratory-data-analysis machine-learning omics-data-integration pipeline pre-processing statistical-analysis user-friendly workflow

11 stars 8.16 score 20 scripts 1 dependents

bioc

debrowser:Interactive Differential Expresion Analysis Browser

Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps.

Maintained by Alper Kucukural. Last updated 5 months ago.

sequencing chipseq rnaseq differentialexpression geneexpression clustering immunooncology

61 stars 7.80 score 65 scripts

bioc

hermes:Preprocessing, analyzing, and reporting of RNA-seq data

Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.

Maintained by Daniel Sabanés Bové. Last updated 5 months ago.

rnaseq differentialexpression normalization preprocessing qualitycontrol rna-seq statistical-engineering

11 stars 7.77 score 48 scripts 1 dependents

bioc

DEXSeq:Inference of differential exon usage in RNA-Seq

The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

Maintained by Alejandro Reyes. Last updated 1 months ago.

immunooncology sequencing rnaseq differentialexpression alternativesplicing differentialsplicing geneexpression visualization

7.75 score 330 scripts 6 dependents

bioc

countsimQC:Compare Characteristic Features of Count Data Sets

countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared.

Maintained by Charlotte Soneson. Last updated 3 months ago.

microbiome rnaseq singlecell experimentaldesign qualitycontrol reportwriting visualization immunooncology

27 stars 7.69 score 24 scripts

bioc

phantasus:Visual and interactive gene expression analysis

Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported.

Maintained by Alexey Sergushichev. Last updated 5 months ago.

geneexpression gui visualization datarepresentation transcriptomics rnaseq microarray normalization clustering differentialexpression principalcomponent immunooncology

43 stars 7.68 score 15 scripts

sssydysss

TransProR:Analysis and Visualization of Multi-Omics Data

A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.

Maintained by Dongyue Yu. Last updated 1 months ago.

190 stars 7.59 score 34 scripts

bioc

TBSignatureProfiler:Profile RNA-Seq Data Using TB Pathway Signatures

Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility.

Maintained by Aubrey R. Odom. Last updated 3 months ago.

geneexpression differentialexpression bioconductor-package biomarkers gene-signatures tuberculosis

12 stars 7.25 score 23 scripts

bioc

regionReport:Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results

Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results.

Maintained by Leonardo Collado-Torres. Last updated 3 months ago.

differentialexpression sequencing rnaseq software visualization transcription coverage reportwriting differentialmethylation differentialpeakcalling immunooncology qualitycontrol bioconductor derfinder deseq2 edger regionreport rmarkdown

9 stars 7.22 score 46 scripts

bioc

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

bioc

RiboCrypt:Interactive visualization in genomics

R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development.

Maintained by Michal Swirski. Last updated 3 days ago.

software sequencing riboseq rnaseq

5 stars 7.08 score 22 scripts

bioc

isomiRs:Analyze isomiRs and miRNAs from small RNA-seq

Characterization of miRNAs and isomiRs, clustering and differential expression.

Maintained by Lorena Pantano. Last updated 5 months ago.

mirna rnaseq differentialexpression clustering immunooncology analyze-isomirs bioconductor isomirs

8 stars 6.97 score 43 scripts

bioc

animalcules:Interactive microbiome analysis toolkit

animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights.

Maintained by Jessica McClintock. Last updated 5 months ago.

microbiome metagenomics coverage visualization

55 stars 6.95 score 23 scripts

bioc

DEFormats:Differential gene expression data formats converter

Convert between different data formats used by differential gene expression analysis tools.

Maintained by Andrzej Oleś. Last updated 5 months ago.

immunooncology differentialexpression geneexpression rnaseq sequencing transcription

4 stars 6.82 score 78 scripts 1 dependents

bioc

ideal:Interactive Differential Expression AnaLysis

This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. Support for reproducibility of the whole analysis is provided by means of a template report which gets automatically compiled and can be stored/shared.

Maintained by Federico Marini. Last updated 3 months ago.

immunooncology geneexpression differentialexpression rnaseq sequencing visualization qualitycontrol gui genesetenrichment reportwriting shinyapps bioconductor differential-expression reproducible-research rna-seq rna-seq-analysis shiny user-friendly

29 stars 6.78 score 5 scripts

bioc

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

bioc

vidger:Create rapid visualizations of RNAseq data in R

The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR.

Maintained by Brandon Monier. Last updated 5 months ago.

immunooncology visualization rnaseq differentialexpression geneexpression data-munging differential-expression gene-expression rna-seq-analysis

19 stars 6.61 score 27 scripts

bioc

HTSFilter:Filter replicated high-throughput transcriptome sequencing data

This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions.

Maintained by Andrea Rau. Last updated 5 months ago.

sequencing rnaseq preprocessing differentialexpression geneexpression normalization immunooncology

6.24 score 58 scripts 1 dependents

bioc

ReportingTools:Tools for making reports in various formats

The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools.

Maintained by Jason A. Hackney. Last updated 5 months ago.

immunooncology software visualization microarray rnaseq go datarepresentation genesetenrichment

6.23 score 93 scripts 1 dependents

bioc

affycoretools:Functions useful for those doing repetitive analyses with Affymetrix GeneChips

Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see.

Maintained by James W. MacDonald. Last updated 5 months ago.

reportwriting microarray onechannel geneexpression

6.07 score 117 scripts

bioc

metaseqR2:An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms

Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way.

Maintained by Panagiotis Moulos. Last updated 19 days ago.

software geneexpression differentialexpression workflowstep preprocessing qualitycontrol normalization reportwriting rnaseq transcription sequencing transcriptomics bayesian clustering cellbiology biomedicalinformatics functionalgenomics systemsbiology immunooncology alternativesplicing differentialsplicing multiplecomparison timecourse dataimport atacseq epigenetics regression proprietaryplatforms genesetenrichment batcheffect chipseq

7 stars 6.05 score 3 scripts

bioc

kissDE:Retrieves Condition-Specific Variants in RNA-Seq Data

Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data.

Maintained by Aurélie Siberchicot. Last updated 5 months ago.

alternativesplicing differentialsplicing experimentaldesign genomicvariation rnaseq transcriptomics

3 stars 5.98 score 7 scripts

bioc

mosdef:MOSt frequently used and useful Differential Expression Functions

This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports.

Maintained by Federico Marini. Last updated 3 months ago.

geneexpression software transcription transcriptomics differentialexpression visualization reportwriting genesetenrichment go

5.98 score 4 dependents

lbbe-software

DRomics:Dose Response for Omics

Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.

Maintained by Aurelie Siberchicot. Last updated 27 days ago.

4 stars 5.96 score 7 scripts

bioc

PathoStat:PathoStat Statistical Microbiome Analysis Package

The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis.

Maintained by Solaiappan Manimaran. Last updated 5 months ago.

microbiome metagenomics graphandnetwork microarray patternlogic principalcomponent sequencing software visualization rnaseq immunooncology

8 stars 5.90 score 8 scripts

bioc

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

guokai8

microbial:Do 16s Data Analysis and Generate Figures

Provides functions to enhance the available statistical analysis procedures in R by providing simple functions to analysis and visualize the 16S rRNA data.Here we present a tutorial with minimum working examples to demonstrate usage and dependencies.

Maintained by Kai Guo. Last updated 6 months ago.

software graphandnetwork microbiome microbiome-analysis

13 stars 5.81 score 25 scripts

bioc

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.

annotation structuralprediction functionalprediction geneprediction genomeassembly differentialexpression

10 stars 5.78 score 5 scripts

bioc

benchdamic:Benchmark of differential abundance methods on microbiome data

Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization.

Maintained by Matteo Calgaro. Last updated 4 months ago.

metagenomics microbiome differentialexpression multiplecomparison normalization preprocessing software benchmark differential-abundance-methods

8 stars 5.78 score 8 scripts

bioc

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

bioc

SEtools:SEtools: tools for working with SummarizedExperiment

This includes a set of convenience functions for working with the SummarizedExperiment class. Note that plotting functions historically in this package have been moved to the sechm package (see vignette for details).

Maintained by Pierre-Luc Germain. Last updated 5 months ago.

geneexpression

2 stars 5.64 score 72 scripts

insightsengineering

teal.modules.hermes:RNA-Seq Analysis Modules to Add to a Teal Application

RNA-seq analysis teal modules based on the `hermes` package.

Maintained by Daniel Sabanés Bové. Last updated 1 years ago.

modules rna-seq-analysis shiny

7 stars 5.54 score 32 scripts

eonurk

cinaR:A Computational Pipeline for Bulk 'ATAC-Seq' Profiles

Differential analyses and Enrichment pipeline for bulk 'ATAC-seq' data analyses. This package combines different packages to have an ultimate package for both data analyses and visualization of 'ATAC-seq' data. Methods are described in 'Karakaslar et al.' (2021) <doi:10.1101/2021.03.05.434143>.

Maintained by Onur Karakaslar. Last updated 10 months ago.

atac-seq differential-analysis enrichment-analysis gene-sets

13 stars 5.52 score 51 scripts

tinnlab

RCPA:Consensus Pathway Analysis

Provides a set of functions to perform pathway analysis and meta-analysis from multiple gene expression datasets, as well as visualization of the results. This package wraps functionality from the following packages: Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Love et al. (2014) <doi:10.1186/s13059-014-0550-8>, Robinson et al. (2010) <doi:10.1093/bioinformatics/btp616>, Korotkevich et al. (2016) <arxiv:10.1101/060012>, Efron et al. (2015) <https://CRAN.R-project.org/package=GSA>, and Gu et al. (2012) <https://CRAN.R-project.org/package=CePa>.

Maintained by Ha Nguyen. Last updated 5 months ago.

biobase deseq2 geoquery edger limma rcyjs fgsea browserviz summarizedexperiment annotationdbi rontotools

1 stars 5.50 score 70 scripts

bioc

ChIPQC:Quality metrics for ChIPseq data

Quality metrics for ChIPseq data.

Maintained by Tom Carroll. Last updated 5 months ago.

sequencing chipseq qualitycontrol reportwriting

5.45 score 140 scripts

bioc

CleanUpRNAseq:Detect and Correct Genomic DNA Contamination in RNA-seq Data

RNA-seq data generated by some library preparation methods, such as rRNA-depletion-based method and the SMART-seq method, might be contaminated by genomic DNA (gDNA), if DNase I disgestion is not performed properly during RNA preparation. CleanUpRNAseq is developed to check if RNA-seq data is suffered from gDNA contamination. If so, it can perform correction for gDNA contamination and reduce false discovery rate of differentially expressed genes.

Maintained by Haibo Liu. Last updated 4 months ago.

qualitycontrol sequencing geneexpression

5 stars 5.44 score 4 scripts

bioc

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 16 days ago.

software sequencing rnaseq geneexpression transcription differentialexpression principalcomponent genesetenrichment pathways batcheffect functionalgenomics visualization dataimport functionalprediction geneprediction go dge enrichment-analysis metaanalysis plots proteins public-data surface surfaceome

3 stars 5.43 score 3 scripts

bioc

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

UMI4Cats:UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data

UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay.

Maintained by Mireia Ramos-Rodriguez. Last updated 5 months ago.

qualitycontrol preprocessing alignment normalization visualization sequencing coverage chromatin chromatin-interaction genomics umi4c

5 stars 5.40 score 7 scripts

bioc

iSEEde:iSEE extension for panels related to differential expression analysis

This package contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels or modes facilitating the analysis of differential expression results. This package does not perform differential expression. Instead, it provides methods to embed precomputed differential expression results in a SummarizedExperiment object, in a manner that is compatible with interactive visualisation in iSEE applications.

Maintained by Kevin Rue-Albrecht. Last updated 5 months ago.

software infrastructure differentialexpression bioconductor hacktoberfest iseeu

1 stars 5.38 score 15 scripts

bioc

diffUTR:diffUTR: Streamlining differential exon and 3' UTR usage

The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases.

Maintained by Pierre-Luc Germain. Last updated 5 months ago.

geneexpression

6 stars 5.38 score 9 scripts

bioc

GeDi:Defining and visualizing the distances between different genesets

The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage.

Maintained by Annekathrin Nedwed. Last updated 5 months ago.

gui genesetenrichment software transcription rnaseq visualization clustering pathways reportwriting go kegg reactome shinyapps

1 stars 5.36 score 22 scripts

bioc

TEKRABber:An R package estimates the correlations of orthologs and transposable elements between two species

TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes.

Maintained by Yao-Chung Chen. Last updated 1 months ago.

differentialexpression normalization transcription geneexpression bioconductor cpp

3 stars 5.33 score 18 scripts

bioc

HybridExpress:Comparative analysis of RNA-seq data for hybrids and their progenitors

HybridExpress can be used to perform comparative transcriptomics analysis of hybrids (or allopolyploids) relative to their progenitor species. The package features functions to perform exploratory analyses of sample grouping, identify differentially expressed genes in hybrids relative to their progenitors, classify genes in expression categories (N = 12) and classes (N = 5), and perform functional analyses. We also provide users with graphical functions for the seamless creation of publication-ready figures that are commonly used in the literature.

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

software functionalgenomics geneexpression transcriptomics rnaseq classification differentialexpression gene-expression hybrid polyploidy rna-seq

14 stars 5.32 score 2 scripts

bioc

DaMiRseq:Data Mining for RNA-seq data: normalization, feature selection and classification

The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

Maintained by Mattia Chiesa. Last updated 5 months ago.

sequencing rnaseq classification immunooncology openjdk

5.32 score 7 scripts 1 dependents

bioc

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

bioc

MultiRNAflow:An R package for integrated analysis of temporal RNA-seq data with multiple biological conditions

Our R package MultiRNAflow provides an easy to use unified framework allowing to automatically make both unsupervised and supervised (DE) analysis for datasets with an arbitrary number of biological conditions and time points. In particular, our code makes a deep downstream analysis of DE information, e.g. identifying temporal patterns across biological conditions and DE genes which are specific to a biological condition for each time.

Maintained by Rodolphe Loubaton. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription timecourse preprocessing visualization normalization principalcomponent clustering differentialexpression genesetenrichment pathways

6 stars 5.26 score 4 scripts

frederikziebell

RNAseqQC:Quality Control for RNA-Seq Data

Functions for semi-automated quality control of bulk RNA-seq data.

Maintained by Frederik Ziebell. Last updated 9 months ago.

2 stars 5.21 score 27 scripts

bioc

scGPS:A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation)

The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations.

Maintained by Quan Nguyen. Last updated 5 months ago.

singlecell clustering dataimport sequencing coverage openblas cpp

4 stars 5.20 score 7 scripts

bioc

icetea:Integrating Cap Enrichment with Transcript Expression Analysis

icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.

Maintained by Vivek Bhardwaj. Last updated 5 months ago.

immunooncology transcription geneexpression sequencing rnaseq transcriptomics differentialexpression cage expression rna-seq

2 stars 5.08 score 7 scripts

bioc

scQTLtools:An R package for single-cell eQTL analysis and visualization

This package specializes in analyzing and visualizing eQTL at the single-cell level. It can read gene expression matrices or Seurat data, or SingleCellExperiment object along with genotype data. It offers a function for cis-eQTL analysis to detect eQTL within a given range, and another function to fit models with three methods. Using this package, users can also generate single-cell level visualization result.

Maintained by Xiaofeng Wu. Last updated 4 days ago.

software geneexpression geneticvariability snp differentialexpression genomicvariation variantdetection genetics functionalgenomics systemsbiology regression singlecell normalization visualization rna-seq sc-eqtl

3 stars 5.02 score

bioc

GARS:GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

Maintained by Mattia Chiesa. Last updated 5 months ago.

classification featureextraction clustering openjdk

5.00 score 2 scripts

bioc

broadSeq:broadSeq : for streamlined exploration of RNA-seq data

This package helps user to do easily RNA-seq data analysis with multiple methods (usually which needs many different input formats). Here the user will provid the expression data as a SummarizedExperiment object and will get results from different methods. It will help user to quickly evaluate different methods.

Maintained by Rishi Das Roy. Last updated 5 months ago.

geneexpression differentialexpression rnaseq transcriptomics sequencing coverage genesetenrichment go

4 stars 5.00 score 7 scripts

bioc

coseq:Co-Expression Analysis of Sequencing Data

Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided.

Maintained by Andrea Rau. Last updated 5 months ago.

geneexpression rnaseq sequencing software immunooncology

4.98 score 16 scripts

bioc

TCC:TCC: Differential expression analysis for tag count data with robust normalization strategies

This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.

Maintained by Jianqiang Sun. Last updated 5 months ago.

immunooncology sequencing differentialexpression rnaseq

4.91 score 41 scripts

bioc

MLSeq:Machine Learning Interface for RNA-Seq Data

This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.

Maintained by Gokmen Zararsiz. Last updated 5 months ago.

immunooncology sequencing rnaseq classification clustering

4.81 score 27 scripts 1 dependents

bioc

NBAMSeq:Negative Binomial Additive Model for RNA-Seq Data

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.

Maintained by Xu Ren. Last updated 5 months ago.

rnaseq differentialexpression geneexpression sequencing coverage differential-expression gene-expression generalized-additive-models generalized-linear-models negative-binomial-regression splines

2 stars 4.78 score 2 scripts

bioc

mobileRNA:mobileRNA: Investigate the RNA mobilome & population-scale changes

Genomic analysis can be utilised to identify differences between RNA populations in two conditions, both in production and abundance. This includes the identification of RNAs produced by multiple genomes within a biological system. For example, RNA produced by pathogens within a host or mobile RNAs in plant graft systems. The mobileRNA package provides methods to pre-process, analyse and visualise the sRNA and mRNA populations based on the premise of mapping reads to all genotypes at the same time.

Maintained by Katie Jeynes-Cupper. Last updated 5 months ago.

visualization rnaseq sequencing smallrna genomeassembly clustering experimentaldesign qualitycontrol workflowstep alignment preprocessing bioinformatics plant-science

3 stars 4.78 score 2 scripts

hugheylab

limorhyde2:Quantify Rhythmicity and Differential Rhythmicity in Genomic Data

Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.

Maintained by Jake Hughey. Last updated 1 years ago.

4.78 score 2 scripts

bioc

Anaquin:Statistical analysis of sequins

The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls.

Maintained by Ted Wong. Last updated 5 months ago.

immunooncology differentialexpression preprocessing rnaseq geneexpression software

4.65 score 45 scripts

bioc

magpie:MeRIP-Seq data Analysis for Genomic Power Investigation and Evaluation

This package aims to perform power analysis for the MeRIP-seq study. It calculates FDR, FDC, power, and precision under various study design parameters, including but not limited to sample size, sequencing depth, and testing method. It can also output results into .xlsx files or produce corresponding figures of choice.

Maintained by Daoyu Duan. Last updated 5 months ago.

epitranscriptomics differentialmethylation sequencing rnaseq software

4.60 score 40 scripts

bioc

NetActivity:Compute gene set scores from a deep learning framework

#' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models.

Maintained by Carlos Ruiz-Arenas. Last updated 5 months ago.

rnaseq microarray transcription functionalgenomics go geneexpression pathways software

4.59 score 26 scripts

bioc

DELocal:Identifies differentially expressed genes with respect to other local genes

The goal of DELocal is to identify DE genes compared to their neighboring genes from the same chromosomal location. It has been shown that genes of related functions are generally very far from each other in the chromosome. DELocal utilzes this information to identify DE genes comparing with their neighbouring genes.

Maintained by Rishi Das Roy. Last updated 5 months ago.

geneexpression differentialexpression rnaseq transcriptomics

4.48 score 3 scripts 1 dependents

bioc

ERSSA:Empirical RNA-seq Sample Size Analysis

The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset.

Maintained by Zixuan Shao. Last updated 5 months ago.

immunooncology geneexpression transcription differentialexpression rnaseq multiplecomparison qualitycontrol

4.48 score 1 scripts

core-bioinformatics

bulkAnalyseR:Interactive Shiny App for Bulk Sequencing Data

Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user.

Maintained by Ilias Moutsopoulos. Last updated 1 years ago.

27 stars 4.47 score 11 scripts

bioc

PRONE:The PROteomics Normalization Evaluator

High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.

Maintained by Lis Arend. Last updated 10 days ago.

proteomics preprocessing normalization differentialexpression visualization data-analysis evaluation

2 stars 4.41 score 9 scripts

bioc

zitools:Analysis of zero-inflated count data

zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.

Maintained by Carlotta Meyring. Last updated 5 months ago.

software statisticalmethod microbiome

4.40 score 6 scripts

bioc

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

bioc

SeqGSEA:Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing

The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively.

Maintained by Xi Wang. Last updated 5 months ago.

sequencing rnaseq genesetenrichment geneexpression differentialexpression differentialsplicing immunooncology

4.34 score 11 scripts

bioc

CeTF:Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis

This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data).

Maintained by Carlos Alberto Oliveira de Biagi Junior. Last updated 5 months ago.

sequencing rnaseq microarray geneexpression transcription normalization differentialexpression singlecell network regression chipseq immunooncology coverage cpp

4.30 score 9 scripts

bioc

scBFA:A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis.

Maintained by Ruoxin Li. Last updated 5 months ago.

singlecell transcriptomics dimensionreduction geneexpression atacseq batcheffect kegg qualitycontrol

4.30 score 4 scripts

bioc

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

bioc

blacksheepr:Outlier Analysis for pairwise differential comparison

Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare.

Maintained by RugglesLab. Last updated 5 months ago.

sequencing rnaseq geneexpression transcription differentialexpression transcriptomics

4.30 score 6 scripts

loosolab

wilson:Web-Based Interactive Omics Visualization

Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data. This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.

Maintained by Hendrik Schultheis. Last updated 4 years ago.

2 stars 4.30 score 7 scripts

bioc

saseR:Scalable Aberrant Splicing and Expression Retrieval

saseR is a highly performant and fast framework for aberrant expression and splicing analyses. The main functions are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process BAM files to ASpli counts \item \code{\link{convertASpli}} - Get gene, bin or junction counts from ASpli SummarizedExperiment \item \code{\link{calculateOffsets}} - Create an offsets assays for aberrant expression or splicing analysis \item \code{\link{saseRfindEncodingDim}} - Estimate the optimal number of latent factors to include when estimating the mean expression \item \code{\link{saseRfit}} - Parameter estimation of the negative binomial distribution and compute p-values for aberrant expression and splicing } For information upon how to use these functions, check out our vignette at \url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd} and the saseR paper: Segers, A. et al. (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRxiv. \url{https://doi.org/10.1101/2023.06.29.547014}.

Maintained by Alexandre Segers. Last updated 5 months ago.

differentialexpression differentialsplicing regression geneexpression alternativesplicing rnaseq sequencing software

1 stars 4.30 score 1 scripts

bioc

gg4way:4way Plots of Differential Expression

4way plots enable a comparison of the logFC values from two contrasts of differential gene expression. The gg4way package creates 4way plots using the ggplot2 framework and supports popular Bioconductor objects. The package also provides information about the correlation between contrasts and significant genes of interest.

Maintained by Benjamin I Laufer. Last updated 5 months ago.

software visualization differentialexpression geneexpression transcription rnaseq singlecell sequencing

4.30 score 3 scripts

bioc

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

bioc

easier:Estimate Systems Immune Response from RNA-seq data

This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy.

Maintained by Oscar Lapuente-Santana. Last updated 5 months ago.

geneexpression software transcription systemsbiology pathways genesetenrichment immunooncology epigenetics classification biomedicalinformatics regression experimenthubsoftware

4.20 score 16 scripts

bioc

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 12 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

bioc

IntEREst:Intron-Exon Retention Estimator

This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files).

Maintained by Ali Oghabian. Last updated 3 days ago.

software alternativesplicing coverage differentialsplicing sequencing rnaseq alignment normalization differentialexpression immunooncology

4.16 score 12 scripts

bioc

TFHAZ:Transcription Factor High Accumulation Zones

It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters.

Maintained by Gaia Ceddia. Last updated 5 months ago.

software biologicalquestion transcription chipseq coverage

4.00 score 2 scripts

bioc

systemPipeTools:Tools for data visualization

systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives.

Maintained by Daniela Cassol. Last updated 5 months ago.

infrastructure dataimport sequencing qualitycontrol reportwriting experimentaldesign clustering differentialexpression multidimensionalscaling principalcomponent

4.00 score 4 scripts

bioc

octad:Open Cancer TherApeutic Discovery (OCTAD)

OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds.

Maintained by E. Chekalin. Last updated 5 months ago.

classification geneexpression pharmacogenetics pharmacogenomics software genesetenrichment

4.00 score 4 scripts

bioc

consensusDE:RNA-seq analysis using multiple algorithms

This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation.

Maintained by Ashley J. Waardenberg. Last updated 5 months ago.

transcriptomics multiplecomparison clustering sequencing software

4.00 score 10 scripts

bioc

microbiomeExplorer:Microbiome Exploration App

The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation.

Maintained by Janina Reeder. Last updated 5 months ago.

classification clustering geneticvariability differentialexpression microbiome metagenomics normalization visualization multiplecomparison sequencing software immunooncology

4.00 score 8 scripts

bioc

SNPhood:SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data

To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest.

Maintained by Christian Arnold. Last updated 5 months ago.

software

3.90 score 1 scripts

mpallocc

autoGO:Auto-GO: Reproducible, Robust and High Quality Ontology Enrichment Visualizations

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the 'DESeq2' package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.

Maintained by Fabio Ticconi. Last updated 1 months ago.

2 stars 3.90 score

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 25 days ago.

riboseq transcriptomics genesetenrichment generegulation reactome software

3.90 score

bioc

RegEnrich:Gene regulator enrichment analysis

This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking.

Maintained by Weiyang Tao. Last updated 5 months ago.

geneexpression transcriptomics rnaseq twochannel transcription genetarget networkenrichment differentialexpression network networkinference genesetenrichment functionalprediction

3.82 score 22 scripts

bioc

DEsubs:DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments

DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases.

Maintained by Aristidis G. Vrahatis. Last updated 5 months ago.

systemsbiology graphandnetwork pathways kegg geneexpression networkenrichment network rnaseq differentialexpression normalization immunooncology

3.78 score 1 scripts

bioc

cypress:Cell-Type-Specific Power Assessment

CYPRESS is a cell-type-specific power tool. This package aims to perform power analysis for the cell-type-specific data. It calculates FDR, FDC, and power, under various study design parameters, including but not limited to sample size, and effect size. It takes the input of a SummarizeExperimental(SE) object with observed mixture data (feature by sample matrix), and the cell-type mixture proportions (sample by cell-type matrix). It can solve the cell-type mixture proportions from the reference free panel from TOAST and conduct tests to identify cell-type-specific differential expression (csDE) genes.

Maintained by Shilin Yu. Last updated 5 months ago.

software geneexpression dataimport rnaseq sequencing

1 stars 3.70 score 2 scripts

bioc

srnadiff:Finding differentially expressed unannotated genomic regions from RNA-seq data

srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values.

Maintained by Zytnicki Matthias. Last updated 3 months ago.

immunooncology geneexpression coverage smallrna epigenetics statisticalmethod preprocessing differentialexpression cpp

3.70 score 3 scripts

kelliejarcher

ordinalbayes:Bayesian Ordinal Regression for High-Dimensional Data

Provides a function for fitting various penalized Bayesian cumulative link ordinal response models when the number of parameters exceeds the sample size. These models have been described in Zhang and Archer (2021) <doi:10.1186/s12859-021-04432-w>.

Maintained by Kellie J. Archer. Last updated 3 years ago.

jags cpp

1 stars 3.70 score 1 scripts

bioc

pairedGSEA:Paired DGE and DGS analysis for gene set enrichment analysis

pairedGSEA makes it simple to run a paired Differential Gene Expression (DGE) and Differencital Gene Splicing (DGS) analysis. The package allows you to store intermediate results for further investiation, if desired. pairedGSEA comes with a wrapper function for running an Over-Representation Analysis (ORA) and functionalities for plotting the results.

Maintained by Søren Helweg Dam. Last updated 5 days ago.

differentialexpression alternativesplicing differentialsplicing geneexpression immunooncology genesetenrichment pathways rnaseq software transcription

2 stars 3.60 score

bioc

deltaCaptureC:This Package Discovers Meso-scale Chromatin Remodeling from 3C Data

This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments.

Maintained by Michael Shapiro. Last updated 5 months ago.

biologicalquestion statisticalmethod

3.48 score 1 scripts

bioc

vulcan:VirtUaL ChIP-Seq data Analysis using Networks

Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature.

Maintained by Federico M. Giorgi. Last updated 5 months ago.

systemsbiology networkenrichment geneexpression chipseq

3.38 score 12 scripts

bioc

rgsepd:Gene Set Enrichment / Projection Displays

R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group.

Maintained by Karl Stamm. Last updated 5 months ago.

immunooncology software differentialexpression genesetenrichment rnaseq

3.30 score 10 scripts

bioc

EBSEA:Exon Based Strategy for Expression Analysis of genes

Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data.

Maintained by Arfa Mehmood. Last updated 5 months ago.

software differentialexpression geneexpression sequencing

3.30 score 4 scripts

bioc

Rmmquant:RNA-Seq multi-mapping Reads Quantification Tool

RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way.

Maintained by Zytnicki Matthias. Last updated 5 months ago.

geneexpression transcription zlib cpp

3.30 score 5 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

plstenger

Anaconda:Targeted Differential and Global Enrichment Analysis of Taxonomic Rank by Shared Asvs

Targeted differential and global enrichment analysis of taxonomic rank by shared ASVs (Amplicon Sequence Variant), for high-throughput eDNA sequencing of fungi, bacteria, and metazoan. Actually works in two steps: I) Targeted differential analysis from QIIME2 data and II) Global analysis by Taxon Mann-Whitney U test analysis from targeted analysis (I) (I) Estimate variance-mean dependence in count/abundance ASVs data from high-throughput sequencing assays and test for differential represented ASVs based on a model using the negative binomial distribution. (II) NCBITaxon_MWU uses continuous measure of significance (such as fold-change or -log(p-value)) to identify NCBITaxon that are significantly enriches with either up- or down-represented ASVs. If the measure is binary (0 or 1) the script will perform a typical 'NCBITaxon enrichment' analysis based Fisher's exact test: it will show NCBITaxon over-represented among the ASVs that have 1 as their measure. On the plot, different fonts are used to indicate significance and color indicates enrichment with either up (red) or down (blue) regulated ASVs. No colors are shown for binary measure analysis. The tree on the plot is hierarchical clustering of NCBITaxon based on shared ASVs. Categories with no branch length between them are subsets of each other. The fraction next to the category name indicates the fraction of 'good' ASVs in it; 'good' ASVs are the ones exceeding the arbitrary absValue cutoff (option in taxon_mwuPlot()). For Fisher's based test, specify absValue=0.5. This value does not affect statistics and is used for plotting only. The original idea was for genes differential expression analysis from Wright et al (2015) <doi:10.1186/s12864-015-1540-2>; adapted here for taxonomic analysis. The 'Anaconda' package makes it possible to carry out these analyses by automatically creating several graphs and tables and storing them in specially created subfolders. You will need your QIIME2 pipeline output for each kingdom (eg; Fungi and/or Bacteria and/or Metazoan): i) taxonomy.tsv, ii) taxonomy_RepSeq.tsv, iii) ASV.tsv and iv) SampleSheet_comparison.txt (the latter being created by you).

Maintained by Pierre-Louis Stenger. Last updated 2 months ago.

3.18 score

w123yu

sRNAGenetic:Analysis of sRNA Expression Changes During Plant Polyploidization

The most important function of the R package sRNAGenetic is the genetic effects analysis of miRNA after plant polyploidization via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of ELD (expression level domainance) by comparing the total expression of the miRNA in allotetraploids with the expression level in the parent species.

Maintained by Yu qing Wu. Last updated 3 years ago.

1 stars 2.70 score 1 scripts

kechrislab

HeritSeq:Heritability of Gene Expression for Next-Generation Sequencing

Statistical framework to analyze heritability of gene expression based on next-generation sequencing data and simulating sequencing reads. Variance partition coefficients (VPC) are computed using linear mixed effects and generalized linear mixed effects models. Compound Poisson and negative binomial models are included. Reference: Rudra, Pratyaydipta, et al. "Model based heritability scores for high-throughput sequencing data." BMC bioinformatics 18.1 (2017): 143.

Maintained by W. Jenny Shi. Last updated 6 years ago.

2 stars 2.60 score 8 scripts

w123yu

ExpGenetic:Non-Additive Expression Analysis of Hybrid Offspring

Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being.

Maintained by Yuqing Wu. Last updated 2 years ago.

2.00 score

bmgfiasri

HEssRNA:Heritability-Based Estimation of Sample Size for RNA-Seq Data

Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.

Maintained by Sarika Jaiswal. Last updated 3 months ago.

1.00 score