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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Related Experiment Video

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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FOGSAA: Fast Optimal Global Sequence Alignment Algorithm.

Angana Chakraborty1, Sanghamitra Bandyopadhyay

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.

Scientific Reports
|April 30, 2013
PubMed
Summary
This summary is machine-generated.

A new Fast Optimal Global Sequence Alignment Algorithm (FOGSAA) significantly speeds up sequence alignment for nucleotides and proteins. It outperforms the Needleman-Wunsch algorithm and improves alignment quality over heuristic methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Accurate sequence alignment is crucial for understanding biological function and evolution.
  • Existing optimal global alignment methods, like Needleman-Wunsch, can be computationally intensive.
  • Heuristic methods offer speed but often compromise alignment quality.

Purpose of the Study:

  • To introduce a novel algorithm, Fast Optimal Global Sequence Alignment Algorithm (FOGSAA), for rapid and accurate global sequence alignment.
  • To demonstrate FOGSAA's superior performance compared to established optimal and heuristic alignment methods.
  • To evaluate FOGSAA's applicability across various sequence types and scoring schemes.

Main Methods:

  • Development of the Fast Optimal Global Sequence Alignment Algorithm (FOGSAA).
  • Comparative analysis of FOGSAA against the Needleman-Wunsch algorithm for nucleotide and protein sequences.
  • Benchmarking FOGSAA against three heuristic global alignment methods.

Main Results:

  • FOGSAA achieves significant time gains over Needleman-Wunsch: 70-90% for highly similar nucleotides, 54-70% for moderately similar nucleotides, and 25-40% for proteins.
  • For dissimilar sequences, FOGSAA provides an approximate score efficiently.
  • Alignment quality is improved by 23%-53% compared to heuristic methods.

Conclusions:

  • FOGSAA is a computationally efficient and accurate algorithm for optimal global sequence alignment.
  • It is suitable for diverse sequence types (nucleotide, protein) and scoring systems, including affine gap penalties.
  • FOGSAA offers a valuable tool for biological sequence analysis where alignment quality is paramount.