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This article is part of the supplement: Articles selected from posters presented at the Tenth Annual International Conference on Research in Computational Biology

Open Access Open Badges Research

Simultaneous identification of long similar substrings in large sets of sequences

Jürgen Kleffe*, Friedrich Möller and Burghardt Wittig

Author Affiliations

Institut für Molekularbiologie und Bioinformatik, Charite-Universitätsmedizin Berlin, Arnimallee 22, 14195 Berlin, Germany

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BMC Bioinformatics 2007, 8(Suppl 5):S7  doi:10.1186/1471-2105-8-S5-S7

Published: 24 May 2007



Sequence comparison faces new challenges today, with many complete genomes and large libraries of transcripts known. Gene annotation pipelines match these sequences in order to identify genes and their alternative splice forms. However, the software currently available cannot simultaneously compare sets of sequences as large as necessary especially if errors must be considered.


We therefore present a new algorithm for the identification of almost perfectly matching substrings in very large sets of sequences. Its implementation, called ClustDB, is considerably faster and can handle 16 times more data than VMATCH, the most memory efficient exact program known today. ClustDB simultaneously generates large sets of exactly matching substrings of a given minimum length as seeds for a novel method of match extension with errors. It generates alignments of maximum length with a considered maximum number of errors within each overlapping window of a given size. Such alignments are not optimal in the usual sense but faster to calculate and often more appropriate than traditional alignments for genomic sequence comparisons, EST and full-length cDNA matching, and genomic sequence assembly. The method is used to check the overlaps and to reveal possible assembly errors for 1377 Medicago truncatula BAC-size sequences published at webcite.


The program ClustDB proves that window alignment is an efficient way to find long sequence sections of homogenous alignment quality, as expected in case of random errors, and to detect systematic errors resulting from sequence contaminations. Such inserts are systematically overlooked in long alignments controlled by only tuning penalties for mismatches and gaps.

ClustDB is freely available for academic use.