Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Identifying Representative Trends in Massive Time Series Data Sets Using Sketches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Effective periodic pattern mining in time series databases
Expert Systems with Applications: An International Journal
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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DNA sequence is an important determinant of the positioning, stability, and activity of nucleosome, yet the molecular basis of these remains elusive. Positioned nucleosomes are believed to play an important role in transcriptional regulation and for the organization of chromatin in cell nuclei. After completing the genome project of many organisms, sequence mining received considerable and increasing attention. Many works devoted a lot of effort to detect the periodicity in DNA sequences, namely, the DNA segments that wrap the Histone protein. In this paper, we describe and apply a dynamic periodicity detection algorithm to discover periodicity in DNA sequences. Our algorithm is based on suffix tree as the underlying data structure. The proposed approach considers the periodicity of alternative substrings, in addition to considering dynamic window to detect the periodicity of certain instances of substrings. We demonstrate the applicability and effectiveness of the proposed approach by reporting test results on three data sets.