ViQuaS: an improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing.

MOTIVATION: The combined effect of a high replication rate and the low fidelity of the viral polymerase in most RNA viruses and some DNA viruses results in the formation of a viral quasispecies. Uncovering information about quasispecies populations significantly benefits the study of disease progression, antiviral drug design, vaccine design and viral pathogenesis. We present a new analysis pipeline called ViQuaS for viral quasispecies spectrum reconstruction using short next-generation sequencing reads. ViQuaS is based on a novel reference-assisted de novo assembly algorithm for constructing local haplotypes. A significantly extended version of an existing global strain reconstruction algorithm is also used. RESULTS: Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1-53.9% in recall, 0-12.1% in precision and 0-38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006-0.143 in KL-divergence and 0.001-0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological sample and a real HIV-1 dataset and demonstrated better results than other methods available.

Microbial and viral metagenomes of a subtropical freshwater reservoir subject to climatic disturbances.

Extreme climatic activities, such as typhoons, are widely known to disrupt our natural environment. In particular, studies have revealed that typhoon-induced perturbations can result in several long-term effects on various ecosystems. In this study, we have conducted a 2-year metagenomic survey to investigate the microbial and viral community dynamics associated with environmental changes and seasonal variations in an enclosed freshwater reservoir subject to episodic typhoons. We found that the microbial community structure and the associated metagenomes continuously changed, where microbial richness increased after typhoon events and decreased during winter. Among the environmental factors that influenced changes in the microbial community, precipitation was considered to be the most significant. Similarly, the viral community regularly showed higher relative abundances and diversity during summer in comparison to winter, with major variations happening in several viral families including Siphoviridae, Myoviridae, Podoviridae and Microviridae. Interestingly, we also found that the precipitation level was associated with the terrestrial viral abundance in the reservoir. In contrast to the dynamic microbial community (L-divergence 0.73 ± 0.25), we found that microbial metabolic profiles were relatively less divergent (L-divergence 0.24 ± 0.04) at the finest metabolic resolution. This study provides for the first time a glimpse at the microbial and viral community dynamics of a subtropical freshwater ecosystem, adding a comprehensive set of new knowledge to aquatic environments.

Energy efficient sensor scheduling with a mobile sink node for the target tracking application.

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performance.