We introduce methods that rapidly evaluate a battery of information-theoretic and algorithmic complexity measures on DNA sequences in application to potential binding sites for nucleosomes. The first application of this new tool demonstrates structure beyond GC content on DNA sequences in the context of nucleosome binding. We tested the measures on well-studied genomic sequences of size 20K and 100K bps . The measures reveal the known in vivo versus in vitro predictive discrepancies , but they also uncover the internal structure of G and C within the nucleosome length , thus disclosing more than simply GC content when one examines alphabet transformations that separate and scramble the GC content signal and the DNA sequence . Most current prediction methods are based upon training (e.g. k-mer discovery), the one here advanced, however, is a training-free approach to investigating informative measures of DNA information content in connection with structural nucleosomic packing .
We introduce and study a set of training-free methods of information-theoretic and algorithmic complexity nature applied to DNA sequences to identify their potential capabilities to determine nucleosomal binding sites. We test our measures on well-studied genomic sequences of different sizes drawn from different sources . The measures reveal the known in vivo versus in vitro predictive discrepancies and uncover their potential to pinpoint (high) nucleosome occupancy. We explore different possible signals within and beyond the nucleosome length and find that complexity indices are informative of nucleosome occupancy. We compare against the gold standard (Kaplan model) and find similar and complementary results with the main difference that our sequence complexity approach. For example, for high occupancy, complexity-based scores outperform the Kaplan model for predicting binding representing a significant advancement in predicting the highest nucleosome occupancy following a training-free approach .
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methods that rapidly evaluate a battery of
and study a set of training-free methods of
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measures on DNA sequences in application to potential binding sites for nucleosomes. The first application of this new tool demonstrates structure beyond GC content on DNA sequences in the context of nucleosome binding. We tested the
nature applied to DNA sequences to identify their potential capabilities to determine nucleosomal binding sites. We test our
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size 20K and 100K bps
different sizes drawn from different sources
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, but they also uncover the internal structure of G and C within
and uncover their potential to pinpoint (high) nucleosome occupancy. We explore different possible signals within and beyond
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, thus disclosing more than simply GC content when one examines alphabet transformations that separate and scramble the GC content signal and the DNA sequence . Most current prediction methods are based upon training (e.g. k-mer discovery), the one here advanced, however, is a
and find that complexity indices are informative of nucleosome occupancy. We compare against the gold standard (Kaplan model) and find similar and complementary results with the main difference that our sequence complexity approach. For example, for high occupancy, complexity-based scores outperform the Kaplan model for predicting binding representing a significant advancement in predicting the highest nucleosome occupancy following a
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to investigating informative measures of DNA information content in connection with structural nucleosomic packing