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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Algorithms for locating extremely conserved elements in multiple sequence alignments
Huei-Hun E Tseng1, Martin Tompa
1Department of Computer Science and Engineering, University of Washington, Box 352350, Seattle, WA 98195-2350, USA. lachesis@cs.washington.edu
Researchers generalized ultraconserved elements to "Extremely Conserved Elements" across multiple species. An efficient algorithm identifies these elements when conserved across all species, aiding genomic research.
Area of Science:
- Genomics
- Bioinformatics
- Evolutionary Biology
Background:
- Ultraconserved elements (UCEs) are genomic sequences conserved across human, mouse, and rat.
- The discovery of UCEs in 2004 spurred significant research into conserved genomic regions.
Purpose of the Study:
- To generalize the concept of UCEs to "Extremely Conserved Elements" (ECEs) conserved across arbitrary sets of species.
- To develop and apply an efficient algorithm for identifying ECEs.
Main Methods:
- Developed a linear-time algorithm to find ECEs conserved across all species in a multiple sequence alignment.
- Demonstrated the NP-completeness of finding ECEs conserved across arbitrary subsets of species.
- Applied the linear-time algorithm to a 44-vertebrate whole-genome alignment.
Main Results:
- Catalogued 177 ECEs within the 44-vertebrate alignment using the efficient algorithm.
- Showed that identifying ECEs conserved across arbitrary subsets of species is computationally complex (NP-complete).
- Identified ECEs share characteristics with previously discovered UCEs.
Conclusions:
- An efficient algorithm exists for identifying ECEs conserved across all or most aligned species, proving practical for research.
- The computational difficulty of finding ECEs across arbitrary subsets highlights the complexity of evolutionary conservation patterns.
- The identified ECEs provide valuable insights into conserved genomic regions and their evolutionary significance.
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