Non-coding regions of the Ebola virus genome contain indispensable phylogenetic and evolutionary information

We compared the numbers of nucleotide substitutions occurring in the non-coding regions and coding regions of Ebola virus genomes and found that non-coding regions contain indispensable phylogenetic and evolutionary information. The omission of genetic data from non-coding regions can lead to unreliable phylogenies and inaccurate estimates of evolutionary parameters.

Mining salt stress-related genes in Spartina alterniflora via analyzing co-evolution signal across 365 plant species using phylogenetic profiling

With the increasing number of sequenced species,phylogenetic profiling(PP)has become a powerful method to predict functional genes based on co-evolutionary information.However,its potential in plant genomics has not yet been fully explored.In this context,we combined the power of machine learning and PP to identify salt stress-related genes in a halophytic grass,Spartina alterniflora,using evolutionary information generated from 365 plant species.Our results showed that the genes highly co-evolved with known salt stress-related genes are enriched in biological processes of ion transport,detoxification and metabolic pathways.For ion transport,five identified genes coding two sodium and three potassium transporters were validated to be able to uptake Na?.In addition,we identified two orthologs of trichome-related AtR3-MYB genes,SaCPC1 and SaCPC2,which may be involved in salinity responses.Genes co-evolved with SaCPCs were enriched in functions related to the circadian rhythm and abiotic stress responses.Overall,this work demonstrates the feasibility of mining salt stress-related genes using evolutionary information,highlighting the potential of PP as a valuable tool for plant functional genomics.

Adaptive candidate estimation-assisted multi-objective particle swarm optimization

The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity.