Next-generation sequencing is rapidly transforming our ability to profile the 
transcriptional, genetic, and epigenetic states of a cell. In particular, 
sequencing DNA from the immunoprecipitation of protein-DNA complexes (ChIP-seq) 
and methylated DNA (MeDIP-seq) can reveal the locations of protein binding sites 
and epigenetic modifications. These approaches contain numerous biases which may 
significantly influence the interpretation of the resulting data. Rigorous 
computational methods for detecting and removing such biases are still lacking. 
Also, multi-sample normalization still remains an important open problem. This 
theoretical paper systematically characterizes the biases and properties of 
ChIP-seq data by comparing 62 separate publicly available datasets, using 
rigorous statistical models and signal processing techniques. Statistical 
methods for separating ChIP-seq signal from background noise, as well as 
correcting enrichment test statistics for sequence-dependent and sonication 
biases, are presented. Our method effectively separates reads into signal and 
background components prior to normalization, improving the signal-to-noise 
ratio. Moreover, most peak callers currently use a generic null model which 
suffers from low specificity at the sensitivity level requisite for detecting 
subtle, but true, ChIP enrichment. The proposed method of determining a cell 
type-specific null model, which accounts for cell type-specific biases, is shown 
to be capable of achieving a lower false discovery rate at a given significance 
threshold than current methods.