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Platform Design Info for The Manufacturer's Name Rice.
Platform Design Info for Affymetrix HuGene-2_1-st.
Platform Design Info for Affymetrix ZebGene-1_1-st.
Affymetrix Affymetrix Porcine Array annotation data (chip porcine) assembled using data from public repositories.
Platform Design Info for The Manufacturer's Name RAE230B.
Platform Design Info for Affymetrix SoyGene-1_0-st.
Platform Design Info for Affymetrix RaGene-1_1-st-v1.
Platform Design Info for The Manufacturer's Name Bsubtilis.
PartheenMetaData http://swegene.onk.lu.se Annotation Data (PartheenMetaData) assembled using data from public repositories.
Platform Design Info for Affymetrix ChiGene-1_0-st.
build graphs from pathway databases, render them by Rgraphviz.
Platform Design Info for Affymetrix Clariom_S_Human.
This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data.
This package contains R functions to predict biological variables to from placnetal DNA methylation data generated from infinium arrays. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data.
Platform Design Info for The Manufacturer's Name Hu6800.
This package provides a general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019.
Platform Design Info for Affymetrix CanGene-1_1-st.
Platform Design Info for Affymetrix RUSGene-1_0-st.
Platform Design Info for Affymetrix EquGene-1_0-st.
Platform Design Info for Affymetrix HuEx-1_0-st-v2.
Platform Design Info for Affymetrix FinGene-1_0-st.
Subsets of Promoter Capture Hi-C data conveniently packaged for Chicago users. Data includes interactions detected for chromosomes 20 and 21 in GM12878 cells and for chromosomes 18 and 19 in mESC.
This package provides a fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs.
Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles.