Tools   Select Another Resource Report Type

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

Software package that includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number aberrations.

Proper citation: ChAMP (RRID:SCR_012891) Copy   

•   Source: SciCrunch Registry

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

Statistical tools for the analysis of ChIP-seq data.

Proper citation: CSAR (RRID:SCR_012930) Copy   

•   Source: SciCrunch Registry

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

Software for power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process.

Proper citation: CSSP (RRID:SCR_012932) Copy   

•   Source: SciCrunch Registry

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

Software package that provides functions to identify genomic regions of interest based on segmented copy number data from multiple samples.

Proper citation: cghMCR (RRID:SCR_012898) Copy   

•   Source: SciCrunch Registry

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

Software using a non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp).

Proper citation: RankProd (RRID:SCR_013046) Copy   

•   Source: SciCrunch Registry

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

Software developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq).

Proper citation: MEDIPS (RRID:SCR_012996) Copy   

•   Source: SciCrunch Registry

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

Software providing a fast scatterplot smoother suitable for microarray normalization based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available.

Proper citation: TurboNorm (RRID:SCR_012963) Copy   

•   Source: SciCrunch Registry

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

Software package that facilitates the primary analysis of ChIP-chip data.

Proper citation: Ringo (RRID:SCR_012973) Copy   

•   Source: SciCrunch Registry

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

Software for handling and analysis of high-throughput microbiome census data.

Proper citation: phyloseq (RRID:SCR_013080) Copy   

•   Source: SciCrunch Registry

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

Software package that is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data.

Proper citation: BayesPeak (RRID:SCR_013011) Copy   

•   Source: SciCrunch Registry

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

Software that identifies protein binding sites from ChIP-seq and nucleosome positioning experiments.

Proper citation: ChIPseqR (RRID:SCR_013016) Copy   

•   Source: SciCrunch Registry

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

Software functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects.

Proper citation: aCGH (RRID:SCR_013232) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/2.1/bioc/html/arrayMagic.html

Software providing a collection of utilities for quality control and processing of two-colour cDNA microarray data

Proper citation: arrayMagic (RRID:SCR_010933) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/2.6/bioc/html/DEGseq.html

R package to identify differentially expressed genes from RNA-Seq data.

Proper citation: DEGseq (RRID:SCR_008480) Copy   

•   Source: SciCrunch Registry

https://www.bioconductor.org/packages/release/bioc/html/GSVA.html

Open source software R package for assaying variation of gene set enrichment over sample population.Used for microarray and RNA-seq data analysis. Gene set enrichment method that estimates variation of pathway activity over sample population in unsupervised manner.

Proper citation: GSVA (RRID:SCR_021058) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/SimFFPE/

Software R package to simulate artifact chimeric reads specifically generated in next generation sequencing process of formalin fixed paraffin embedded tissue. Simulates normal reads as well as artifact chimeric reads that are enriched in FFPE samples. These artifact chimeric reads can lead to large amounts of false positive structural variant calls.

Proper citation: SimFFPE (RRID:SCR_021085) Copy   

•   Source: SciCrunch Registry

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

Software R package for statistical analysis of phenotypic data.Tool kit for standardized analysis of high throughput phenotypic data.

Proper citation: PhenStat (RRID:SCR_021317) 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

http://www.fhcrc.org/

Fred Hutchinson Cancer Research Center and Seattle Cancer Care Alliance (SCCA) have merged to form Fred Hutchinson Cancer Center, unified adult cancer research and care center. Independent, nonprofit organization is clinically integrated part of UW Medicine and is UW Medicine’s cancer program.

Proper citation: Fred Hutchinson Cancer Center (RRID:SCR_004984) Copy   

•   Source: SciCrunch Registry

http://master.bioconductor.org/packages/2.13/bioc/html/BHC.html

Software package that performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data and also includes a randomised algorithm which is more efficient for larger data sets.

Proper citation: BHC (RRID:SCR_006399) Copy   

•   Source: SciCrunch Registry