Utilization of systematic error-assessment software to improve phylogenetic accuracy.

Unlike classical systems based on the use of morphological data, modern phylogenetic analyses use genetic information to construct phylogenetic trees. Ongoing research in the field of phylogenetics is evaluating the accuracy of phylogenetic estimation results and the reliability of phylogenetic trees to explain evolutionary relationships. Recently, the probability of stochastic errors in large-scale phylogenetic datasets has decreased, while the probability of systematic errors has increased. Therefore, before constructing a phylogenetic tree, it is necessary to assess the causes of systematic bias to improve the accuracy of phylogenetic estimates. We performed analyses of three datasets (Terebelliformia, Daphniid, and Glires clades) using bioinformatics software to assess systematic error and improve phylogenetic tree accuracy. Then, we proposed a combination of systematic biases capable of discerning the most suitable gene markers within a series of taxa and generating conflicting phylogenetic topologies. Our findings will help improve the reliability of phylogenetic software to estimate phylogenies more accurately by exploiting systematic bias.