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Multiple sequence alignment using Probcons and Probalign.

Usman Roshan1

  • 1Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.

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Summary
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This chapter details Probcons and Probalign, two key bioinformatics programs for sequence alignment. They utilize hidden Markov models and partition function approaches to achieve high accuracy in biological sequence analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence alignment is crucial for understanding protein and DNA relationships.
  • Numerous computational strategies exist for sequence alignment.
  • Probcons and Probalign represent significant advancements in alignment accuracy.

Purpose of the Study:

  • To describe the Probcons and Probalign software.
  • To highlight their methodologies in sequence alignment.
  • To discuss their contributions to bioinformatics benchmarks.

Main Methods:

  • Probcons: Maximum expected accuracy alignment using hidden Markov models (HMMs).
  • Probalign: Maximum expected accuracy alignment using a partition function approach.
  • Comparison against established protein multiple alignment benchmarks.

Main Results:

  • Probcons achieved state-of-the-art accuracy on protein alignment benchmarks upon its release.
  • Probalign built upon Probcons' strategy with a different probabilistic model.
  • Both programs offer robust solutions for accurate sequence alignment.

Conclusions:

  • Probcons and Probalign are foundational tools in bioinformatics for accurate sequence alignment.
  • Their distinct approaches (HMMs vs. partition functions) offer valuable alternatives.
  • The chapter provides an overview of these influential alignment programs.