Predicting nucleosome positioning using a duration Hidden Markov Model

Liqun Xi, Yvonne Fondufe-Mittendorf, Lei Xia, Jared Flatow, Jonathan Widom, Ji Ping Wang

Research output: Contribution to journalArticlepeer-review

100 Scopus citations


Background: The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function.Results: We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download.Conclusions: Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

Original languageEnglish
Article number346
JournalBMC Bioinformatics
StatePublished - Jun 24 2010

Bibliographical note

Funding Information:
The research is supported by NIH grant R01GM075313 and NCI grant U54CA143869. We acknowledge with gratitude the gift of a parallel sequencing run from Roche/454 Life Sciences, and thank Ms. Jolene Osterberger (Roche/454 Life Sciences) and Dr. Nadereh Jafari (Northwestern University) for arranging this. The authors would also like to thank Drs. Eran Segal, Guocheng Yuan, Zhiping Weng and Yutao Fu for help in providing data and their codes.

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics


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