Algorithms that minimize putative synapomorphy in an alignment cannot be directly implemented since trivial cases withconcatenated sequences would be selected because they would imply a minimum number of events to be explained (e.g., asingle insertion/deletion would be required to explain divergence among two sequences). Therefore, indirect measures to approachparsimony 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 putativesynapomorphies. We also present a Genetic Algorithm that uses GLOCSA as the objective function to produce sequence alignmentsrefining alignments previously generated by additional existing alignment tools (we recommend MUSCLE). We show that in theexample cases our GLOCSA-guided Genetic Algorithm (GGGA) does improve the GLOCSA values, resulting in alignments thatimply less putative synapomorphies.
展开▼
