Origin and transmission of bovine viral diarrhea virus type 1 in China revealed by phylodynamic analysis.

Bovine viral diarrhea virus type 1 (BVDV-1) is prevalent worldwide and causes significant economic loss in animal husbandry. Since its first report in the 1980s in China, several genotypes of BVDV-1 had been reported, but an in-depth phylogenetic analysis on the BVDV isolates from China is lacking. To investigate the molecular evolution and phylodynamics of BVDV-1 genotypes circulating in China, comprehensive phylogenetic and phylodynamic analyses were performed to reconstruct the origin and spatial-temporal distribution, and to trace main viral flows among different areas. BVDV-1 5'-UTR sequences from China and Mongolia were collected from Genbank, and the phylogeny was built using the maximum likelihood method. The Bayesian Skygrid was used to estimate the evolution and population dynamics of BVDV-1. Eight BVDV-1 genotypes were identified, of which 1b and 1 m are the main genotypes. The results indicated that BVDV-1 might be introduced in China in the 1960s, and after a long period of population growth, it gradually leveled off after 2010. The phylodynamic inference clearly shows a more steady BVDV-1 population growth, and the transmission of BVDV-1 may be confined to specific regions. This study will help to understand the molecular epidemiology and long-term evolutionary dynamics of BVDV-1 in China, therefore providing a scientific basis for the prevention and controlof the virus.

Phylodynamic and transmission pattern of rabies virus in China and its neighboring countries.

Rabies is a fatal disease caused by infection with rabies virus (RABV), and human rabies is still a critical public-health concern in China. Although there have been some phylogenetic studies about RABV transmission patterns, with the accumulation of more rabies sequences in recent years, there is an urgent need to update and clarify the spatial and temporal patterns of RABV circulating in China on a national scale. In this study, we collected all available RABV nucleoprotein gene sequences from China and its neighboring countries and performed comparative analysis. We identified six significant subclades of RABV circulating in China and found that each of them has a specific geographical distribution, reflecting possible physical barriers to gene flow. The phylogeographic analysis revealed minimal viral movement among different geographical locations. An analysis using Bayesian coalescent methods indicated that the current RABV strains in China may come from a common ancestor about 400 years ago, and currently, China is amid the second event of increasing RABV population since the 1950s, but the population has decreased gradually. We did not detect any evidence of recombination in the sequence dataset, nor did we find any evidence for positive selection during the expansion of RABV. Overall, geographic location and neutral genetic drift may be the main factors in shaping the phylogeography of RABV transmission in China.

An incremental clustering method based on the boundary profile.

Many important applications continuously generate data, such as financial transaction administration, satellite monitoring, network flow monitoring, and web information processing. The data mining results are always evolving with the newly generated data. Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all of the data from scratch. The incremental clustering approach is an essential way to solve the problem of clustering with growing Big Data. This paper proposes a boundary-profile-based incremental clustering (BPIC) method to find arbitrarily shaped clusters with dynamically growing datasets. This method represents the existing clustering results with a collection of boundary profiles and discards the inner points of clusters rather than keep all data. It greatly saves both time and space storage costs. To identify the boundary profile, this paper presents a boundary-vector-based boundary point detection (BV-BPD) algorithm that summarizes the structure of the existing clusters. The BPIC method processes each new point in an online fashion and updates the clustering results in a batch mode. When a new point arrives, the BPIC method either immediately labels it or temporarily puts it into a bucket according to the relationship between the new data and the boundary profiles. A bucket is employed to distinguish the noise from the potential seeds of new clusters and alleviate the effects of data order. When the bucket is full, the BPIC method will cluster the data within it and update the clustering results. Thus, the BPIC method is insensitive to noise and the order of new data, which is critical for the robustness of the incremental clustering process. In the experiments, the performance of the boundary point detection algorithm BV-BPD is compared with the state-of-the-art method. The results show that the BV-BPD is better than the state-of-the-art method. Additionally, the performance of BPIC and other two incremental clustering methods are investigated in terms of clustering quality, time and space efficiency. The experimental results indicate that the BPIC method is able to get a qualified clustering result on a large dataset with higher time and space efficiency.

An improved alignment-free model for DNA sequence similarity metric.

BACKGROUND: DNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words' probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality. RESULTS: This paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings. CONCLUSIONS: The following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.