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H-CLAP: hierarchical clustering within a linear array with an application in genetics.

Samiran Ghosh, Jeffrey P Townsend

    Statistical Applications in Genetics and Molecular Biology
    |March 25, 2015
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    We developed a new method, hierarchical clustering in a linear array (H-CLAP), for clustering marked sites in linear biological sequences like DNA. This Bayesian approach effectively identifies patterns and boundaries in ordered data.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Clustering typically assumes unconstrained data distributions.
    • Linear biological sequences (DNA, RNA, proteins) present unique clustering challenges due to directional constraints and fixed positions.
    • Existing clustering methods are often unsuitable for ordered, non-exchangeable data points.

    Purpose of the Study:

    • To develop an adjusted clustering method for marked sites within linear arrays.
    • To address the limitations of traditional clustering for directionally constrained data.
    • To identify natural partitioning and significant cluster boundaries in linear sequence data.

    Main Methods:

    • Developed a hierarchical Bayesian approach tailored for linear arrays.
    • Employed a Markov clustering algorithm to reveal patterns in marked sites.
    • Introduced the hierarchical clustering in a linear array (H-CLAP) algorithm, incorporating domain-specific directional constraints.

    Main Results:

    • The H-CLAP method successfully clusters marked sites in linear arrays.
    • The Bayesian approach offers greater flexibility in cluster discovery compared to standard algorithms.
    • The method provides both hierarchical clustering and biologically significant cluster boundaries.

    Conclusions:

    • H-CLAP is an effective and flexible method for clustering data in linear arrays.
    • The algorithm's ability to incorporate directional constraints makes it suitable for biological sequence analysis.
    • The identified cluster boundaries may hold significant biological implications.