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Statistical Identification of Uniformly Mutated Segments within Repeats

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Combinatorial Pattern Matching (CPM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2373))

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Abstract

Given a long string of characters from a constant size (w.l.o.g. binary) alphabet we present an algorithm to determine whether its characters have been generated by a single i.i.d. random source. More specifically, consider all possible k-coin models for generating a binary string S, where each bit of S is generated via an independent toss of one of the k coins in the model. The choice of which coin to toss is decided by a random walk on the set of coins where the probability of a coin change is much lower than the probability of using the same coin repeatedly. We present a statistical test procedure which, for any given S, determines whether the a posteriori probability for k = 1 is higher than for any other k > 1. Our algorithm runs in time O(l 4 log l), where l is the length of S, through a dynamic programming approach which exploits the convexity of the a posteriori probability for k.

The problem we consider arises from two critical applications in analyzing long alignments between pairs of genomic sequences. A high alignment score between two DNA sequences usually indicates an evolutionary relationship, i.e. that the sequences have been generated as a result of one or more copy events followed by random point mutations. Such sequences may include functional regions (e.g. exons) as well as nonfunctional ones (e.g. introns). Functional regions with critical importance exhibit much lower mutation rates than non-functional DNA (or DNA

Supported in part by an NSF Career Award and by Charles B. Wang Foundation.

Partially supported by the IST Programme of the EU under contract number IST-1999-14186 (ALCOM-FT).

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Author information

Authors and Affiliations

  1. Dept of EECS, Dept of Genetics and Center for Computational Genomics, CWRU, USA

    S. Cenk Ṣahinalp

  2. Dept of Genetics and Center for Computational Genomics, CWRU, USA

    Evan Eichler

  3. Dept of Computer Science, University of Warwick, UK

    Paul Goldberg

  4. School of Computing, Simon Fraser University, Canada

    Petra Berenbrink

  5. Pacific Institute of Mathematics, Simon Fraser University, Canada

    Tom Friedetzky

  6. NEC Research Institute and Dept of EECS, CWRU, USA

    Funda Ergun

Authors
  1. S. Cenk Ṣahinalp
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  2. Evan Eichler
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  3. Paul Goldberg
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  4. Petra Berenbrink
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  5. Tom Friedetzky
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  6. Funda Ergun
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Editor information

Editors and Affiliations

  1. Department of Electrial Engineering and Computer Science, University of Padova, Via Gradenigo 6/A, 35131, Padova, Italy

    Alberto Apostolico

  2. Department of Informatics, Kyushu University, Fukuoka 812-8581, Japan

    Masayuki Takeda

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© 2002 Springer-Verlag Berlin Heidelberg

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Ṣahinalp, S.C., Eichler, E., Goldberg, P., Berenbrink, P., Friedetzky, T., Ergun, F. (2002). Statistical Identification of Uniformly Mutated Segments within Repeats. In: Apostolico, A., Takeda, M. (eds) Combinatorial Pattern Matching. CPM 2002. Lecture Notes in Computer Science, vol 2373. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45452-7_21

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  • DOI: https://doi.org/10.1007/3-540-45452-7_21

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43862-5

  • Online ISBN: 978-3-540-45452-6

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