A linear-time algorithm for studying genetic variation

Nikola Stojanovic, Piotr Berman

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The study of variation in DNA sequences, within the framework of phylogeny or population genetics, for instance, is one of the most important subjects in modern genomics. We here present a new lineartime algorithm for finding maximal k-regions in alignments of three sequences, which can be used for the detection of segments featuring a certain degree of similarity, as well as the boundaries of distinct genomic environments such as gene clusters or haplotype blocks, k-regions are defined as these which have a center sequence whose Hamming distance from any of the alignment rows is at most k, and their determination in the general case is known to be NP-hard.

    Original languageEnglish (US)
    Title of host publicationAlgorithms in Bioinformatics - 6th International Workshop, WABI 2006, Proceedings
    PublisherSpringer Verlag
    Pages344-354
    Number of pages11
    ISBN (Print)3540395830, 9783540395836
    DOIs
    StatePublished - Jan 1 2006
    Event6th International Workshop on Algorithms in Bioinformatics, WABI 2006 - Zurich, Switzerland
    Duration: Sep 11 2006Sep 13 2006

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume4175 LNBI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other6th International Workshop on Algorithms in Bioinformatics, WABI 2006
    CountrySwitzerland
    CityZurich
    Period9/11/069/13/06

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • Computer Science(all)

    Fingerprint Dive into the research topics of 'A linear-time algorithm for studying genetic variation'. Together they form a unique fingerprint.

  • Cite this

    Stojanovic, N., & Berman, P. (2006). A linear-time algorithm for studying genetic variation. In Algorithms in Bioinformatics - 6th International Workshop, WABI 2006, Proceedings (pp. 344-354). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4175 LNBI). Springer Verlag. https://doi.org/10.1007/11851561_32