Phylogenetic networks are used to estimate evolutionary relationships among biological entities or taxa involving reticulate events such as horizontal gene transfer, hybridization, recombination, and reassortment. In the past decade, many phylogenetic tree and network reconstruction methods have been proposed. Despite that they are highly accurate in reconstructing simple to moderate complex reticulate events, the performance decreases when several reticulate events are present simultaneously. In this paper, we proposed QS-Net, a phylogenetic network reconstruction method taking advantage of information on the relationship among six taxa. To evaluate the performance of QS-Net, we conducted experiments on three artificial sequence data simulated from an evolutionary tree, an evolutionary network involving three reticulate events, and a complex evolutionary network involving five reticulate events. Comparison with popular phylogenetic methods including Neighbor-Joining, Split-Decomposition, Neighbor-Net, and Quartet-Net suggests that QS-Net is comparable with other methods in reconstructing tree-like evolutionary histories, while it outperforms them in reconstructing reticulate events. In addition, we also applied QS-Net in real data including a bacterial taxonomy data consisting of 36 bacterial species and the whole genome sequences of 22 H7N9 influenza A viruses. The results indicate that QS-Net is capable of inferring commonly believed bacterial taxonomy and influenza evolution as well as identifying novel reticulate events. The software QS-Net is publically available at .
Nowadays with the readily accessibility of online social networks (OSNs), people are facilitated to share interesting information with friends through OSNs. Undoubtedly these sharing activities make our life more fantastic. However, meanwhile one challenge we have to face is information overload that we do not have enough time to review all of the content broadcasted through OSNs. So we need to have a mechanism to help users recognize interesting items from a large pool of content. In this project, we aim at filtering unwanted content based on the strength of trust relationships between users. We have proposed two kinds of trust models-basic trust model and source-level trust model. The trust values are estimated based on historical user interactions and profile similarity. We estimate dynamic trusts and analyze the evolution of trust relationships over dates. We also incorporate the auxiliary causes of interactions to moderate the noisy effect of user's intrinsic tendency to perform a certain type of interaction. In addition, since the trustworthiness of diverse information sources are rather distinct, we further estimate trust values at source-level. Our recommender systems utilize several types of Collaborative Filtering (CF) approaches, including conventional CF (namely user-based, item-based, singular value decomposition (SVD)based), and also trust-combined user-based CF. We evaluate our trust models and recommender systems on Friendfeed datasets. By comparing the evaluation results, we found that the recommendations based on estimated trust relationships were better than conventional CF recommendations.
BackgroundMultiple sequence alignment (MSA) is one of the most important research contents in bioinformatics. A number of MSA programs have emerged. The accuracy of MSA programs highly depends on the parameters setting, mainly including gap open penalties (GOP), gap extension penalties (GEP) and substitution matrix (SM). This research tries to obtain the optimal GOP, GEP and SM rather than MAFFT default parameters.ResultsThe paper discusses the MAFFT program benchmarked on BAliBASE3.0 database, and the optimal parameters of MAFFT program are obtained, which are better than the default parameters of CLUSTALW and MAFFT program.ConclusionsThe optimal parameters can improve the results of multiple sequence alignment, which is feasible and efficient.
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