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

1. 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.

2. 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

3. 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.

4. 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.