Tools   Select Another Resource Report Type

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/2.11/bioc/html/easyRNASeq.html

Software that calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as ''RPKM'' or by the ''DESeq'' or ''edgeR'' package.

Proper citation: easyRNASeq (RRID:SCR_012020) Copy   

•   Source: SciCrunch Registry

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

Genotype Calling and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

Proper citation: CRLMM (RRID:SCR_012580) Copy   

•   Source: SciCrunch Registry

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

Software for normalization of Agilent miRNA arrays.

Proper citation: LVSmiRNA (RRID:SCR_012752) Copy   

•   Source: SciCrunch Registry

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

Software package that detects statistically significant difference between read enrichment profiles in different ChIP-Seq samples.

Proper citation: MMDiff (RRID:SCR_012692) Copy   

•   Source: SciCrunch Registry

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

Software package that identifies differential expression in high-throughput ''count'' data, such as that derived from next-generation sequencing machines.

Proper citation: baySeq (RRID:SCR_012795) Copy   

•   Source: SciCrunch Registry

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

A fully scalable online pre-processing algorithm for short oligonucleotide microarray atlases.

Proper citation: RPA (RRID:SCR_012768) Copy   

•   Source: SciCrunch Registry

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

Software that provides an integrated solution for the Illumina microarray data analysis.

Proper citation: lumi (RRID:SCR_012781) 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

http://www.bioconductor.org/packages/devel/bioc/html/GeneNetworkBuilder.html

Software application for discovering direct or indirect targets of transcription factors (TFs) using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, it generates a regulatory network of the TF.

Proper citation: GeneNetworkBuilder (RRID:SCR_006455) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/spotSegmentation/

Model-based software package for processing microarray images so as to estimate foreground and background intensities. The method starts with a very simple but effective automatic gridding method, and then proceeds in two steps. The first step applies model-based clustering to the distribution of pixel intensities, using the Bayesian Information Criterion (BIC) to choose the number of groups up to a maximum of three. The second step is spatial, finding the large spatially connected components in each cluster of pixels. The method thus combines the strengths of the histogram-based and spatial approaches. It deals effectively with inner holes in spots and with artifacts. It also provides a formal inferential basis for deciding when the spot is blank, namely when the BIC favors one group over two or three.

Proper citation: spotSegmentation (RRID:SCR_001298) 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

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://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