Skip to main navigation Skip to search Skip to main content

New t-gap insertion-deletion-like metrics for DNA hybridization thermodynamic modeling

  • Arkadii G. D'Yachkov
  • , Anthony J. Macula
  • , Wendy K. Pogozelski
  • , Thomas E. Renz
  • , Vyacheslav V. Rykov
  • , David C. Torney

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We discuss the concept of t-gap block isomorphic subsequences and use it to describe new abstract string metrics that are similar to the Levenshtein insertion-deletion metric. Some of the metrics that we define can be used to model a thermodynamic distance function on single-stranded DNA sequences. Our model captures a key aspect of the nearest neighbor thermodynamic model for hybridized DNA duplexes. One version of our metric gives the maximum number of stacked pairs of hydrogen bonded nucleotide base pairs that can be present in any secondary structure in a hybridized DNA duplex without pseudoknots. Thermodynamic distance functions are important components in the construction of DNA codes, and DNA codes are important components in biomolecular computing, nanotechnology, and other biotechnical applications that employ DNA hybridization assays. We show how our new distances can be calculated by using a dynamic programming method, and we derive a Varshamov-Gilbert-like lower bound on the size of some of codes using these distance functions as constraints. We also discuss software implementation of our DNA code design methods.

Original languageEnglish
Pages (from-to)866-881
Number of pages16
JournalJournal of Computational Biology
Volume13
Issue number4
DOIs
StatePublished - May 2006

Keywords

  • Common subsequence
  • DNA code
  • DNA hybridization
  • Insertion-deletion
  • Nearest neighbor thermodynamics
  • Stacked pairs
  • Weighted sequences
  • t-gap block isomorphic subsequences

Fingerprint

Dive into the research topics of 'New t-gap insertion-deletion-like metrics for DNA hybridization thermodynamic modeling'. Together they form a unique fingerprint.

Cite this