Inst. f. Informatik   
Uni Leipzig

Bioinformatics Preprint 05-004

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Multiple Sequence Alignments of Partially Coding Nucleic Acid Sequences

Roman R. Stocsits, Ivo L. Hofacker, Claudia Fried, Peter F. Stadler


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.
Methods: The standard scoring scheme for nucleic acid alignments can be extended to incorporate simultaneously information on translation products in one or more reading frames.
Results: 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.
Conclusions: 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.
Availability: The source code and documentation may be downloaded from and href="">

multiple sequence alignment, partially coding sequences, overlapping reading frames, RNA virus genomes, Hox genes.

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