Raw Input

Laplacian of Gaussian (LOG)

Nucleosome Calls on Yeast/ Human

LogitBoost

Top 30 Features

Sequence Representation

FFN Algorithm

Pattern Discovered

Nucleosome Positioning Data are from Lee et al. (yeast) and Schones et al. (human)

Nucleosome-containing sequences identified by LOG

LogitBoost selected the most relevant features in yeast and human.

We applied FFN algorithm and identified 88 NCS-specific patterns in yeast, 2328 patterns in human resting status and 589 in human activated status.

• Different feature combinations are predictive for different nucleosome-forming and nucleosome-depletion sequences.• Nucleosomes in the same promoters usually exhibit different feature

patterns• Nucleosome-occupancy prediction is location-dependent: the farther

away from TSSs, the more accurate in the sequence based prediction in human.• Structural features frequently appear in feature patterns

Low Expressed Genes contain more NCSs exhibiting feature patterns.• In all three conditions (yeast, human resting and activated), the

average scores for nucleosomes in low expressed genes are larger than the scores for corresponding nucleosomes in highly expressed genes.• This is consistent with the hypothesis that the lack of transcriptional

activities can lead to sequence-determined nucleosome-forming events.

Conserved and different Patterns in human and yeast• 580 out of 589 feature patterns are conserved in T cell resting and

activated data.• 35 out of 88 features patterns discovered in yeast are conserved in

human resting and human activated data.• 41 conserved features patterns across yeast, human resting and

activated T cells.• 53 patterns discovered in yeast only suggests that different feature

combinations may help nucleosome occupancy prediction for different species.