spurthy111's Stars
macs3-project/MACS
MACS -- Model-based Analysis of ChIP-Seq
Bioconductor/CSAMA2015
Course material for CSAMA 2015: Statistics and Computing in Genome Data Science
stephenturner/qqman
An R package for creating Q-Q and manhattan plots from GWAS results
mcanouil/IMA2
Illumina Methylation Analyzer 2 for ILLUMINA 450k HumanMethylation Beadchip
Bonder-MJ/450K_DataProcessing
Complete pipeline to process Illumina 450K methylation data.
datasciencelabs/2016
Course material for BIO 260 and CSCI E-107 in Spring 2016
hansenlab/minfi
Devel repository for minfi
bylin/nealelab-scripts
Reusable code written for Neale Lab. Includes Biopython extensions, SGE support, output file parsing, etc.
JIC-CSB/jicbioimage.transform
Tool for automated and reproducible bioimage analysis
JIC-CSB/scientific-linux
Provisioning of core scientific software into a Linux VM.
arq5x/bedtools2
bedtools - the swiss army knife for genome arithmetic
jdblischak/singleCellSeq
Batch effects and the effective design of single-cell gene expression studies
aaronwolen/gibbs-brain-project
Re-analysis of genomic data generated by Gibbs et al 2010 (PMID: 20485568)
jmacdon/methylation
Package for working with Illumina methylation arrays
obigriffith/biostar-tutorials
Code for biostar tutorials - mostly R and Perl
shenlab-sinai/diffreps
Differential analysis for ChIP-seq with biological replicates
TiphaineCMartin/coMET_original
The coMET package is a web-based plotting tool and R-based package to visualize different genome-wide association scans such as EWAS (epigenome-wide association scan) results in a genomic region of interest. coMET provides a plot of the EWAS association signal and visualisation of the methylation correlation between CpG sites (co-methylation). The coMET package also provides the option to annotate the region using functional genomic information, including both user-defined features and pre-selected features based on the Encode project. The plot can be customized with different parameters, such as plot labels, colours, symbols, heatmap colour scheme, significance thresholds, and including reference CpG sites. Finally, the tool can also be applied to display the correlation patterns of other genomic data, e.g. gene expression array data.
bioinformatics-core-shared-training/ngs-in-bioc
A course on Analysing Next Generation (/High Throughput etc..) Sequencing data using Bioconductor