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Related Experiment Videos

Multiple sequence alignments of partially coding nucleic acid sequences.

Roman R Stocsits1, Ivo L Hofacker, Claudia Fried

  • 1Interdisciplinary Centre for Bioinformatics, University of Leipzig, Haertelstrasse 16-18, D-04107 Leipzig, Germany. roman@bioinf.uni-leipzig.de

BMC Bioinformatics
|June 30, 2005
PubMed
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This study introduces codaln, a novel tool for aligning RNA and DNA sequences using both nucleic acid and amino acid information. This combined approach improves alignment quality for genomic data analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-quality sequence alignment is crucial for comparative genomics.
  • Nucleic acid sequences show greater heterogeneity than protein sequences due to genetic code redundancy.
  • Aligning coding nucleic acid sequences using amino acid information is desirable, especially with partial translations or overlapping reading frames.

Purpose of the Study:

  • To develop a multiple alignment tool incorporating both nucleic acid and amino acid sequence information.
  • To address challenges in aligning coding sequences with complex translation patterns, such as in RNA viruses.

Main Methods:

  • Developed codaln, a tool implementing a combined nucleic acid and amino acid scoring model.
  • Supports pairwise and progressive multiple alignments with flexible parameter weighting.

Related Experiment Videos

  • Resource requirements are comparable to standard tools like ClustalW.

    • Main Results:

    • Demonstrated codaln's applicability to diverse sequences like bacteriophage Levivirus and Vertebrate Hox clusters.
    • Showed that combining nucleic acid and amino acid data significantly improves alignment quality.
    • Validated the tool's effectiveness in enhancing downstream analyses.

    Conclusions:

    • The combined scoring model in codaln offers superior alignment accuracy for coding sequences.
    • Improved alignments enhance the performance of tools dependent on accurate sequence data, such as RNA secondary structure prediction.
    • codaln represents a valuable advancement for genomic sequence analysis.