Abstract
Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases with concatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., a single insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approach parsimony need to be implemented. In this paper, we thoroughly present a Global Criterion for Sequence Alignment (GLOCSA) that uses a scoring function to globally rate multiple alignments aiming to produce matrices that minimize the number of putative synapomorphies. We also present a Genetic Algorithm that uses GLOCSA as the objective function to produce sequence alignments refining alignments previously generated by additional existing alignment tools (we recommend MUSCLE). We show that in the example cases our GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA values, resulting in alignments that imply less putative synapomorphies.
Highlights
The use of DNA or protein sequences for different purposes has greatly increased as the technology for DNA and protein sequencing has improved with the consequent cost reduction
GLOCSA-guided Genetic Algorithm (GGGA) is capable of improving alignments generated by other tools (MUSCLE v3.6 [3] was used in this work to prealign the matrices)
Global Criterion for Sequence Alignment (GLOCSA) is composed of three individual criteria: Mean Column Homogeneity (MCH), Reciprocal of Gap Blocks (RGB) and Columns Increment (CI)
Summary
The use of DNA (deoxyribonucleic acid) or protein sequences for different purposes has greatly increased as the technology for DNA and protein sequencing has improved with the consequent cost reduction. The first step to make this information manageable is to device tools to identify comparable proteins or DNA fragments, as well as comparable protein or DNA sequence units (amino acids and nucleotides, resp.). This process is referred to as sequence alignment. We propose an evolutionary computation technique suitable to optimize it This novel objective function is coupled with a Genetic Algorithm (GA), the GLOCSA-Guided Genetic Algorithm (GGGA), which uses a compact representation of the alignments and five different mutation operators to explore the solution landscape. GGGA is capable of improving alignments generated by other tools (MUSCLE (multiple sequence comparison by log-expectation) v3.6 [3] was used in this work to prealign the matrices)
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