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http://bioinf.wehi.edu.au/limma/

Software package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.

Proper citation: LIMMA (RRID:SCR_010943) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 30,2023. Software for differential gene expression analysis based on the negative binomial distribution. It estimates variance-mean dependence in count data from high-throughput sequencing assays and tests for differential expression.

Proper citation: DESeq (RRID:SCR_000154) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Software for modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays.

Proper citation: MIMOSA (RRID:SCR_000184) Copy   

•   Source: SciCrunch Registry

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

Software that uses a Bayesian hierarchical mixture model to learn correlation patterns of allele-specificity among multiple proteins.

Proper citation: iASeq (RRID:SCR_000420) Copy   

•   Source: SciCrunch Registry

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

An R/Bioconductor package that interfaces the X!Tandem protein identification algorithm.

Proper citation: rTANDEM (RRID:SCR_000409) Copy   

•   Source: SciCrunch Registry

https://github.com/yongchao/flowPeaks

Software for fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means.

Proper citation: flowPeaks (RRID:SCR_000407) Copy   

•   Source: SciCrunch Registry

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

Software that provides quality control and quality assessment tools for gated flow cytometry data.

Proper citation: QUALIFIER (RRID:SCR_000389) Copy   

•   Source: SciCrunch Registry

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

Software package to infer gene regulatory networks from time-series measurements. The algorithm is expected to be useful in reconstruction of small biological directed networks from time course data.

Proper citation: TDARACNE (RRID:SCR_000498) Copy   

•   Source: SciCrunch Registry

http://mfuzz.sysbiolab.eu/

Software package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Mfuzz (RRID:SCR_000523) Copy   

•   Source: SciCrunch Registry

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

Software for identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists.

Proper citation: Rdisop (RRID:SCR_000453) Copy   

•   Source: SciCrunch Registry

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

Software that discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data.

Proper citation: FlipFlop (RRID:SCR_000625) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.13/bioc/html/bsseq.html

R package with tools for analyzing and visualizing bisulfite sequencing data.

Proper citation: bsseq (RRID:SCR_001072) Copy   

•   Source: SciCrunch Registry

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

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software R package written for analysis of murine palate miRNA two-color expression data.

Proper citation: MmPalateMiRNA (RRID:SCR_001070) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.10/bioc/html/R453Plus1Toolbox.html

R software toolbox of functions for the analysis of data generated by Roche's 454 sequencing platform. Additional functions are included for quality assurance, annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. A pipeline for the detection of structural variants is provided.

Proper citation: R453Plus1Toolbox (RRID:SCR_001105) Copy   

•   Source: SciCrunch Registry

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

R package that contains functions to merge microRNA and respective targets using different databases.

Proper citation: RmiR (RRID:SCR_001069) Copy   

•   Source: SciCrunch Registry

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

R software that detects enriched regions of ChIP-seq data sets jointly.

Proper citation: Jmosaics (RRID:SCR_001094) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/devel/bioc/html/massiR.html

Software that predicts the sex of samples in gene expression microarray datasets.

Proper citation: massiR (RRID:SCR_001157) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/sRAP/

Software package that provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data.

Proper citation: sRAP (RRID:SCR_001297) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/yaqcaffy/

Software package for quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets.

Proper citation: yaqcaffy (RRID:SCR_001295) Copy   

•   Source: SciCrunch Registry

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

Software package that uses a modeling strategy especially designed for normalizing high-throughput genomic data. The premise is that your data is a function of study-specific variables which are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest.

Proper citation: SNM (RRID:SCR_001299) Copy   

•   Source: SciCrunch Registry