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Alignment Free Dissimilarities for Nucleosome Classification

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

Epigenetic mechanisms such as nucleosome positioning, histone modifications and DNA methylation play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have shown a role of DNA sequences in recruitment of epigenetic regulators. For this reason, the use of more suitable similarities or dissimilarity between DNA sequences could help in the context of epigenetic studies. In particular, alignment-free dissimilarities have already been successfully applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. In this work, we focalize the study on the problem of nucleosome classification, providing a benchmark study of 6 alignment free dissimilarity measures between sequences, belonging to the categories of geometric-based, correlation-based, information-based and compression based. Their comparisons have been done versus an alignment based dissimilarity, by measuring the performance of several nearest neighbour classifiers that incorporate each one the considered dissimilarities. Results computed on three dataset of nucleosome forming and inhibiting sequences, shows that among the alignment free dissimilarities, the geometric and correlation are the more suitable for the purpose of nucleosome classification, making them a more efficient alternative to the alignment-based similarity measures, which nevertheless are yet the preferred choice when dealing with sequence similarity measurements.

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Acknowledgments

Part of this work was carried out using instruments provided by the Euro-Mediterranean Institute of Science and Technology, and funded with the Italian National Operational Programme for Research and Competitiveness 2007–2013 grant awarded to the project titled “CyberBrain-Polo di innovazione” (Project code: PONa3_00210, European Regional Development Fund).

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Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università Degli Studi di Palermo, Palermo, Italy

    Giosué Lo Bosco

  2. Dipartimento di Scienze per L’Innovazione e le Tecnologie Abilitanti, Istituto Euro Mediterraneo di Scienza e Tecnologia, Palermo, Italy

    Giosué Lo Bosco

Authors
  1. Giosué Lo Bosco
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Correspondence to Giosué Lo Bosco .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

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Lo Bosco, G. (2016). Alignment Free Dissimilarities for Nucleosome Classification. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-44332-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44331-7

  • Online ISBN: 978-3-319-44332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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