Center for Bioinformatics and Molecular Biostatistics

Nucleosomes, the fundamental repeating subunits of all eukaryotic chromatin, are responsible for packaging DNA into chromosomes inside the cell nucleus and controlling gene expression. While it has been well established that nucleosomes exhibit higher affinity for select DNA sequences, until recently it was unclear whether such preferences exerted a significant, genome-wide effect on nucleosome positioning in vivo. This question was seemingly and recently resolved in the affirmative: a wide-ranging series of experimental and computational analyses provided extensive evidence that the instructions for wrapping DNA around nucleosomes are contained in the DNA itself. This subsequently labelled second genetic code was based on data-driven, structural, and biophysical considerations. It was subjected to an extensive suite of validation procedures, with one conclusion being that intrinsic, genome-encoded, nucleosome organization explains _50% of in vivo nucleosome positioning. Here, we revisit both the nature of the underlying sequence preferences, and the performance of the proposed code. A series of new analyses, employing spectral envelope (Fourier transform) methods for assessing key sequence periodicities, classification techniques for evaluating predictive performance, and discriminatory motif finding methods for devising alternate models, are applied. The findings from the respective analyses indicate that signature dinucleotide periodicities are absent from the bulk of the high affinity nucleosome-bound sequences, and that the predictive performance of the code is modest. We conclude that further exploration of the role of sequence-based preferences in genome-wide nucleosome positioning is warranted. This work offers a methodologic counterpart to a recent, high resolution determination of nucleosome positioning that also questions the accuracy of the proposed code and, further, provides illustration of techniques useful in assessing sequence periodicity and predictive performance.