Journal Article

Exon discovery by genomic sequence alignment

Burkhard Morgenstern, Oliver Rinner, Saïd Abdeddaïm, Dirk Haase, Klaus F. X. Mayer, Andreas W. M. Dress and Hans-Werner Mewes

in Bioinformatics

Volume 18, issue 6, pages 777-787
Published in print June 2002 | ISSN: 1367-4803
Published online June 2002 | e-ISSN: 1460-2059 | DOI:
Exon discovery by genomic sequence alignment

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Motivation: During evolution, functional regions in genomic sequences tend to be more highly conserved than randomly mutating ‘junk DNA’ so local sequence similarity often indicates biological functionality. This fact can be used to identify functional elements in large eukaryotic DNA sequences by cross-species sequence comparison. In recent years, several gene-prediction methods have been proposed that work by comparing anonymous genomic sequences, for example from human and mouse. The main advantage of these methods is that they are based on simple and generally applicable measures of (local) sequence similarity; unlike standard gene-finding approaches they do not depend on species-specific training data or on the presence of cognate genes in data bases. As all comparative sequence-analysis methods, the new comparative gene-finding approaches critically rely on the quality of the underlying sequence alignments.

Results: Herein, we describe a new implementation of the sequence-alignment program DIALIGN that has been developed for alignment of large genomic sequences. We compare our method to the alignment programs PipMaker, WABA and BLAST and we show that local similarities identified by these programs are highly correlated to protein-coding regions. In our test runs, PipMaker was the most sensitive method while DIALIGN was most specific.

Availability: The program is downloadable from the DIALIGN home page at

Contact: burkhard@TechFak.Uni-Bielefeld.DE


To whom correspondence should be addressed at Universität Bielefeld, Technische Fakultät, Praktische Informatik, Postfach 100131, 33501 Bielefeld, Germany.

Journal Article.  0 words. 

Subjects: Bioinformatics and Computational Biology

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