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Open Access Software

Multiple sequence alignments of partially coding nucleic acid sequences

Roman R Stocsits1, Ivo L Hofacker2*, Claudia Fried3 and Peter F Stadler1234

Author Affiliations

1 Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstraße 16-18, D-04107 Leipzig, Germany

2 Institute for Theoretical Chemistry, University of Vienna, Währingerstraße 17, A-1090 Wien, Austria

3 Bioinformatics Group, Department of Computer Science, University of Leipzig, Haertelstraße 16-18, D-04107 Leipzig, Germany

4 Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe NM 87501, USA

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BMC Bioinformatics 2005, 6:160  doi:10.1186/1471-2105-6-160

Published: 28 June 2005

Abstract

Background

High quality sequence alignments of RNA and DNA sequences are an important prerequisite for the comparative analysis of genomic sequence data. Nucleic acid sequences, however, exhibit a much larger sequence heterogeneity compared to their encoded protein sequences due to the redundancy of the genetic code. It is desirable, therefore, to make use of the amino acid sequence when aligning coding nucleic acid sequences. In many cases, however, only a part of the sequence of interest is translated. On the other hand, overlapping reading frames may encode multiple alternative proteins, possibly with intermittent non-coding parts. Examples are, in particular, RNA virus genomes.

Results

The standard scoring scheme for nucleic acid alignments can be extended to incorporate simultaneously information on translation products in one or more reading frames. Here we present a multiple alignment tool, codaln, that implements a combined nucleic acid plus amino acid scoring model for pairwise and progressive multiple alignments that allows arbitrary weighting for almost all scoring parameters. Resource requirements of codaln are comparable with those of standard tools such as ClustalW.

Conclusion

We demonstrate the applicability of codaln to various biologically relevant types of sequences (bacteriophage Levivirus and Vertebrate Hox clusters) and show that the combination of nucleic acid and amino acid sequence information leads to improved alignments. These, in turn, increase the performance of analysis tools that depend strictly on good input alignments such as methods for detecting conserved RNA secondary structure elements.