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E Coward

Publications and source records attributed to E Coward.

4 recordsLinked to original sources

Detecting localized repeats in genomic sequences: a new strategy and its application to Bacillus subtilis and Arabidopsis thaliana sequences.

A new method for the search of local repeats in long DNA sequences, such as complete genomes, is presented. It detects a large variety of repeats varying in length from one to several hundred bases, which may contain many mutations. By mutations we mean substitutions, insertions or deletions of one or more bases. The method is based on counting occurrences of short words (3-12 bases) in sequence fragments called windows. A score is computed for each window, based on calculating exact word occurrence probabilities for all the words of a given length in the window. The probabilities are defined using a Bernoulli model (independent letters) for the sequence, using the actual letter frequencies from each window. A plot of the probabilities along the sequence for high-scoring windows facilitates the identification of the repeated patterns. We applied the method to the 1.87 Mb sequence of chromosome 4 of Arabidopsis thaliana and to the complete genome of Bacillus subtilis (4.2 Mb). The repeats that we found were classified according to their size, number of occurrences, distance between occurrences, and location with respect to genes. The method proves particularly useful in detecting long, inexact repeats that are local, but not necessarily tandem. The method is implemented as a C program called EXCEP, which is available on request from the authors.

Arabidopsis↗

Detecting periodic patterns in biological sequences.

MOTIVATION: The search for repeated patterns in DNA and protein sequences is important in sequence analysis. The rapid increase in available sequences, in particular from large-scale genome sequencing projects, makes it relevant to develop sensitive automatic methods for the identification of repeats. RESULTS: A new method for finding periodic patterns in biological sequences is presented. The method is based on evolutionary distance and 'phase shifts' corresponding to insertions and deletions. A given sequence is aligned to itself in a certain sense, trying to minimize a distance to periodicity. Relationships between different such periodicity measures are discussed. An iterative algorithm is used, and the running time is nearly proportional to the sequence length. The alignment produces a periodic consensus pattern. A 'phase score' is used to indicate a statistical significance of the periodicity. Three examples using both DNA and protein sequences illustrate how the method can be used to find patterns. AVAILABILITY: On request from the authors. CONTACT: evindc@mat nu.no; finn.drablos@unimed.sintef.no

Algorithms↗