#normR - normR obeys regime mixture rules
Normalization and Difference Calling for Next Generation Sequencing (NGS) Experiments via Joint Multinomial Modeling#R 3.2 compliant version
Two NGS tracks are modeled simultaneously by fitting a binomial mixture model on mapped read counts. In the first counting process, a desired smoothing kernel (bin size) and read characteristic threshold (quality, SAMFLAG) can be specified. In a second step a binomial mixture model with a user-specified number of components is fit to the data. The fit yields different enrichment regimes in the supplied NGS tracks. Log-space computation is done in C/C++ where OpenMP enables for fast parallel computation.
To install normR from the working repository, easiest is using devtools:
#install dependencies
source("https://bioconductor.org/biocLite.R")
biocLite("bamsignals", suppressUpdates=T)
#fetch current normR version from github
install.packages("devtools")
require(devtools)
devtools::install_github("your-highness/normr@R3.2")
See the vignette for a toy example on normR usage. The documentation of routines can be accessed from with R with ?.
ChIP-seq normalization / enrichment calling with an Input experiment (Whole Cell Extract, H3/IgG ChIP-seq)
ChIP-seq differential enrichment calling for two different antigens in same sample population
ChIP-seq identification of enrichment regimes to investigate on sample heterogeneity
RNA-seq differential expression calling
ChIP-seq differential enrichment calling in two different samples (be aware of CNVs!)
CNV identification
Be sure to check out the following amazing github projects for your upcoming NGS magic:
bamsignals - Efficient Counting in Indexed Bam Files for Single End and Paired End NGS Data
EpicSeg - Chromatin Segmentation Based on a Probabilistic Multinomial Model for Read Counts
kfoots - Fit Multivariate Discrete Probability Distributions to Count Data
deepTools - User-Friendly Tools for Normalization and Visualization of Deep-Sequencing Data
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