Abstract
BackgroundStudies on the distribution of indel sizes have consistently found that they obey a power law. This finding has lead several scientists to propose that logarithmic gap costs, G (k) = a + c ln k, are more biologically realistic than affine gap costs, G (k) = a + bk, for sequence alignment. Since quick and efficient affine costs are currently the most popular way to globally align sequences, the goal of this paper is to determine whether logarithmic gap costs improve alignment accuracy significantly enough the merit their use over the faster affine gap costs.ResultsA group of simulated sequences pairs were globally aligned using affine, logarithmic, and log-affine gap costs. Alignment accuracy was calculated by comparing resulting alignments to actual alignments of the sequence pairs. Gap costs were then compared based on average alignment accuracy. Log-affine gap costs had the best accuracy, followed closely by affine gap costs, while logarithmic gap costs performed poorly. Subsequently a model was developed to explain the results.ConclusionIn contrast to initial expectations, logarithmic gap costs produce poor alignments and are actually not implied by the power-law behavior of gap sizes, given typical match and mismatch costs. Furthermore, affine gap costs not only produce accurate alignments but are also good approximations to biologically realistic gap costs. This work provides added confidence for the biological relevance of existing alignment algorithms.
Highlights
Studies on the distribution of indel sizes have consistently found that they obey a power law
Sequence alignments are essential to the study of molecular biology and systematics because they purport to reveal regions in sequences that are homologous
How do the best gap costs for each scheme compare to one another? And second, how do the maximum alignment accuracy for each scheme compare to one another for each sequence pair? The first question investigates what happens if researchers use a single gap cost across many alignments, and the second investigates what happens if researchers optimize gap costs to each alignment
Summary
Studies on the distribution of indel sizes have consistently found that they obey a power law. This finding has lead several scientists to propose that logarithmic gap costs, G (k) = a + c ln k, are more biologically realistic than affine gap costs, G (k) = a + bk, for sequence alignment. Researchers usually need to align sequences before they can be studied. There are two main types of alignment algorithms: local and global. Local alignment algorithms like FASTA [3] and BLAST [4] attempt to align only parts of sequences often avoiding gaps, whereas global alignment algorithms like CLUSTAL [5,6] and MCALIGN [7,8] attempt to align entire sequences, explicitly handling gaps.
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