This track displays hit regions and peak centers for Sanger ChIP-chip data, as identified by hidden Markov model (HMM) analysis.
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Data for each replicate was normalized with the Tukey-Biweight Method using R (as recommended by NimbleGen). The log base 2 ratio of the normalized intensities was used for downstream data processing.
A two-state HMM was used to analyze the data. The states of the HMM represent regions of the tile path corresponding to antibody binding locations. State emission probabilities were determined by comparing the cumulative distribution of the experimental data for each replicate on each ENCODE region to a fitted cumulative normal distribution. The fitted distribution was calculated using the Levenberg-Marquart curve-fitting technique and six fitting points ranging from 0.05 to 0.45 of the cumulative distribution. Initial fitting parameters were set from the experimental data. This model is robust through a range of sensible transition probabilities.
Bound regions were identified by finding the optimal state sequence from the HMM using the Viterbi algorithm, and the resulting region data was post-processed to develop the hit list. Hits were defined as contiguous portions of the tile path identified as bound by the HMM. The score of a hit was determined by taking the summation of the median enrichment values of the tiles in the contiguous portions (i.e. the area under the peak). For the purpose of this analysis, hits that were within 1000 base pairs of adjacent hits were combined into hit regions.
The start position of the oligo with the highest enrichment value in the hit region was deemed the center of the peak. The ranking of hits was based on the total score of all hits in a hit region. It is recommended that analysis based on this data use the peak centers expanded to a convenient size for the analysis.
The ChIP-chip data were generated by Ian Dunham's lab at the Sanger Institute. Contacts: Ian Dunham and Christoph Koch.
The HMM analysis was performed at the EBI by Paul Flicek.
Raw data may be downloaded from the Sanger Institute website at ftp://ftp.sanger.ac.uk/pub/encode.