Local similarity search to find gene indicators in mitochondrial genomes.
Given a set of nucleotide sequences we consider the problem of identifying conserved substrings occurring in homologous genes in a large number of sequences. The problem is solved by identifying certain nodes in a suffix tree containing all substrings occurring in the given nucleotide sequences. Due to the large size of the targeted data set, our approach employs a truncated version of suffix trees. Two methods for this task are introduced: (1) The annotation guided marker detection method uses gene annotations which might contain a moderate number of errors; (2) The probability based marker detection method determines sequences that appear significantly more often than expected. The approach is successfully applied to the mitochondrial nucleotide sequences, and the corresponding annotations that are available in RefSeq for 2989 metazoan species. We demonstrate that the approach finds appropriate substrings.
Department of Computer Science, University of Leipzig, Postfach 100920, Leipzig D-04009, Germany. ruby.moritz@informatik.uni-leipzig.de.
Ruby L V
RL
Moritz
Department of Computer Science, University of Leipzig, Postfach 100920, Leipzig D-04009, Germany. bernt@informatik.uni-leipzig.de.
Matthias
M
Bernt
Department of Computer Science, University of Leipzig, Postfach 100920, Leipzig D-04009, Germany. middendorf@informatik.uni-leipzig.de.
Martin
M
Middendorf