Chromatin immunoprecipitation combined with massively parallel sequencing 
methods (ChIP-seq) is becoming the standard approach to study interactions of 
transcription factors (TF) with genomic sequences. At the example of public 
STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of 
ChIP-seq data.We compare recently developed approaches to determine STAT1 
binding sites from ChIP-seq data. Assessing the content of the established 
consensus sequence for STAT1 binding sites, we find that the usage of "negative 
control" ChIP-seq data fails to provide substantial advantages. We derive a 
single refined probabilistic model of STAT1 binding sequences from these 
ChIP-seq data. Contrary to previous claims, we find no evidence that STAT1 binds 
to multiple distinct motifs upon interferon-gamma stimulation in vivo. While a 
large majority of genomic sites with high ChIP-seq signal is associated with a 
nucleotide sequence resembling a STAT1 binding site, only a very small subset of 
the over 5 million potential STAT1 binding sites in the human genome is covered 
by ChIP-seq data. Furthermore a surprisingly large fraction of the ChIP-seq 
signal (5%) is absorbed by a small family of repetitive sequences (MER41). The 
observation of the binding of activated STAT1 protein to a specific repetitive 
element bolsters similar reports concerning p53 and other TFs, and strengthens 
the notion of an involvement of repeats in gene regulation. Incidentally MER41 
are specific to primates, consequently, regulatory mechanisms in the IFN-STAT 
pathway might fundamentally differ between primates and rodents. On a 
methodological aspect, the presence of large numbers of nearly identical binding 
sites in repetitive sequences may lead to wrong conclusions about intrinsic 
binding preferences of TF as illustrated by the spacing analysis STAT1 tandem 
motifs. Therefore, ChIP-seq data should be analyzed independently within 
repetitive and non-repetitive sequences.