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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Published on: July 14, 2015

Sequence progressive alignment, a framework for practical large-scale probabilistic consistency alignment.

Benedict Paten1, Javier Herrero, Kathryn Beal

  • 1Department of Engineering, University of California, Santa Cruz CA, USA. benedict@soe.ucsc.edu

Bioinformatics (Oxford, England)
|December 6, 2008
PubMed
Summary
This summary is machine-generated.

We adapted probabilistic consistency alignment for large genomic sequences using a novel sequence progressive alignment framework. This significantly reduces memory usage and enhances scalability for comparative genomics.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Multiple sequence alignment is crucial for comparative genomics.
  • Existing methods are optimized for small sequences, particularly amino acids.
  • Scaling these methods to large genomic sequences remains a challenge.

Purpose of the Study:

  • To adapt probabilistic consistency alignment for practical, large-scale genomic sequence alignment.
  • To develop a scalable framework for computationally intensive sequence analysis.

Main Methods:

  • Developed a 'sequence progressive alignment' framework.
  • Iteratively computed alignments by processing sequences from left to right.
  • Significantly reduced memory consumption compared to naive implementations.

Main Results:

  • The Pecan program enables practical alignment of large genomic sequences.
  • Demonstrated strong performance in ancient repeat alignments.
  • Pecan is now a default alignment tool in several whole-genome comparative genomics projects.

Conclusions:

  • The sequence progressive alignment framework offers a scalable solution for large genomic data.
  • Engineering insights from this work are valuable for other large-scale sequence analysis algorithms.
  • Pecan provides an efficient and effective tool for comparative genomics.