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http://www.bioconductor.org/packages/release/bioc/html/metahdep.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 18,2025. Software tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies.

Proper citation: metahdep (RRID:SCR_001225) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/genArise.html

An easy to use software tool for dual color microarray data. Its GUI-Tk based environment lets any non-experienced user perform a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer.

Proper citation: genArise (RRID:SCR_001346) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/categoryCompare.html

A software package for meta-analysis of high-throughput experiments using feature annotations. It calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested).

Proper citation: categoryCompare (RRID:SCR_001223) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/globaltest.html

A software package that tests groups of covariates (or features) for association with a response variable. The package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms.

Proper citation: globaltest (RRID:SCR_001256) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.12/bioc/html/rGADEM.html

R package with tools for de novo motif discovery in large-scale genomic sequence data.

Proper citation: rGADEM (RRID:SCR_001091) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.14/bioc/html/MinimumDistance.html

Software package for analysis of de novo copy number variants in trios from high-dimensional genotyping platforms.

Proper citation: MinimumDistance (RRID:SCR_001260) Copy   

•   Source: SciCrunch Registry

http://bios.unc.edu/~weisun/software/asSeq.htm

Software that establishes a statistical framework for future developments of eQTL (expression quantitative trait locus) mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes. This R package has been submitted to R/bioconductor. It will be available on bioconductor soon. It is recommended to install this R package from bioconductor. You can also install this R package from the source code by yourself. Since the R package contains C code, a C complier is required for installation. With both R and appropriate c complier installed, this R package can be installed using the following command (in Mac Terminal window or Windows command window) R CMD INSTALL asSeq

Proper citation: asSeq (RRID:SCR_001625) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://bioinfo.cipf.es/noiseq/doku.php?id=start

Software used for the identification of differentially expressed genes from count data or previously normalized count data. It empirically models the noise distribution of count changes by contrasting fold-change differences (M) and absolute expression differences (D) for all the features in samples within the same condition. This reference distribution is then used to assess whether the M-D values computed between two conditions for a given gene is likely to be part of the noise or represent a true differential expression.

Proper citation: NOISeq (RRID:SCR_003002) Copy   

•   Source: SciCrunch Registry

http://r3cseq.genereg.net/Site/index.html

An R/Bioconductor package to identify chromosomal interaction regions generated by chromosome conformation capture (3C) coupled to next-generation sequencing (NGS), a technique termed 3C-seq. It performs data analysis for a number of different experimental designs, as it can analyze 3C-seq data with or without a control experiment and it can be used to facilitate data analysis for experiments with multiple replicates. The r3Cseq package provides functions to perform data normalization, statistical analysis for cis/trans interactions and visualization in order to help scientists identify genomic regions that physically interact with the given viewpoints of interest. This tool greatly facilitates hypothesis generation and the interpretation of experimental results.

Proper citation: r3Cseq (RRID:SCR_003198) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/EasyqpcR.html

Software package for low-throughput real-time quantitative PCR data analysis. The package allows you to import easily qPCR data files. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors.

Proper citation: EasyqpcR (RRID:SCR_003406) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html

Software as an interactive web tool to compare two or more groups of samples in a Gene Expression Omnibus (GEO) series regardless of data type and quality. Used to identify genes that are differentially expressed across experimental conditions. Results are presented as a table of genes ordered by significance.

Proper citation: GEO2R (RRID:SCR_016569) Copy   

•   Source: SciCrunch Registry

https://visrsoftware.github.io/

Software as an R-based visual framework for analysis of sequencing datasets. Provides a framework for integrative and interactive analyses.

Proper citation: VisR (RRID:SCR_016658) Copy   

•   Source: SciCrunch Registry

https://github.com/PapenfussLab/svaNUMT

Software R package for Nuclear Mitochondrial integration events NUMT detection using structural variant calls.

Proper citation: svaNUMT (RRID:SCR_021381) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/riboSeqR.html

Software tool for analysis of sequencing data from ribosome profiling experiments. Used for plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments.

Proper citation: riboSeqR (RRID:SCR_016947) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Rsubread.html

Software R package for sequence alignment and counting for R. Used for analyses of second and third generation sequencing data, for read mapping, read counting, SNP calling, short and long read alignment, quantification and mutation discovery. Includes assessment of sequence reads, read alignment, read summarization, exon-exon junction detection, fusion detection, detection of short and long indels, absolute expression calling and SNP calling. Can be used with reads generated from any of the major sequencing platforms including Illumina GA/HiSeq/MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM/Proton sequencers.

Proper citation: Rsubread (RRID:SCR_016945) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/scran.html

Software package for low-level analyses of single-cell RNA-seq data. Used for quality control, data exploration and normalization, cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection.

Proper citation: scran (RRID:SCR_016944) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Biostrings.html

Software package for efficient manipulation of biological strings. Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences.

Proper citation: Biostrings (RRID:SCR_016949) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.html

Software written in R for determining cluster count and membership by stability evidence in unsupervised analysis. Provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset with item tracking, item consensus and cluster consensus plots.

Proper citation: ConsensusClusterPlus (RRID:SCR_016954) Copy   

•   Source: SciCrunch Registry

https://github.com/SofieVG/FlowSOM

Software tool to analyze Flow or mass cytometry data using a Self-Organizing Map. Used to obtain an overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise.

Proper citation: FlowSOM (RRID:SCR_016899) Copy   

•   Source: SciCrunch Registry