Peak detection on ChIP-Seq data using wavelet transformation

We propose a signal processing approach for detecting enrichment regions from ChIP-seq datasets. A wavelet transform of the ChIP-seq data offers a direct visualization for both short- and long-range patterns of the genome-wide mapping profile for protein binding site on DNA. To investigate the location of transcription factor binding site (TFBS) from ChIP-seq data, a wavelet-based peak detection algorithm is proposed. Differing from prior methods exploring the statistics of peaks in whole genome, scalogram of raw data is used. In addition, a SNR-like parameter used to detects the peaks is proposed to instead of raw data for tackling the peak finding problem. Also peak depth, the length of peak regions can be obtained by the measurement of SNR-like parameter with a threshold constrain. Furthermore, in order to eliminate false positives, a filter which sifts out the peaks with sufficient SNR but not deep enough in sequence depth is applied. The effectiveness of our method is demonstrated by applying the STAT1 ChIP-seq data and comparing to the well known published method, PeakSeq. The experimental results show that a large fraction of peaks identified by our method are consistent with the results of PeakSeq algorithm while our results show more consistent motif conservation scores.