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#normR - normR obeys regime mixture rules ## Normalization and Difference Calling for Next Generation Sequencing (NGS) Experiments via Joint Multinomial Modeling --- 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. ### Release Version The master branch is always in sync with the [normR Bioconductor release]( and the [normR github Bioconductor mirror]( A R 3.2 compliant version can be found in the [normR R3.2 tree]( ### Installation To install normR from the release repository, easiest way is to use Bioconductor or devtools: ```R #install dependencies if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("bamsignals", suppressUpdates=T) #fetch current normR version from github install.packages("devtools") require(devtools) devtools::install_github("your-highness/normr") ``` ### Usage See the [vignette]( for a toy example on normR usage. The documentation of routines can be accessed from with R with ``?``. #### Use cases * 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 #### Useful links 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