Machine learning predicts nucleosome binding modes of transcription factors.

Detailed analysis of the 24 (out of 106) TFs with both ChIP-seq data and E-MI penetration (lig147) values (Additional file 1 : Supplementary Table S7) revealed a clear trend (Fig. 6 ).

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Examination of the fraction of TFs with predicted end preference reveals an interesting trend in eukaryotes.

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The performance of the binary classifiers generally follows a decreasing trend with increasing subsequence lengths from 3 to 6, in which the classifier with subsequence length 4 outperforms other subsequence lengths (Fig. 3 b, Additional file 2 : Supplementary Figure S1–S3).

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