Fast assessment of the correlation between coverage-like genomic features and its statistical significance

The modern high-throughput sequencing methods provide massive amounts of genome-focused, DNA-positioned data. This data is often represented as a function of the DNA coordinate (e.g. coverage). The genomeor chromosome-wide correlations between data from different sources may provide information about functional biological interrelation of the investigated features, e.g., trancription and histone modification. The task to compute the correlation was already successfully solved for interval annotations ([1]) as well as for coverage (functional) data ([2], [3], [4]). The key idea of the correlation studies is that two features that are similarly distributed along a chromosome may be functionally related. The point we are addressing here is a that peaks of dependent functional features can be located in a similar, although somewhat different, way. To account for these similarities, we propose here a fast method for calculation of kerneled correlation between two numeric annotations of the genome. The kernel represents the mutual position of related features; e.g., a Gaussian shape corresponds to ’somewhere around’, etc.