Collaborative Research: Deciphering the Genetic Diversity of Viruses through Gene Networks

点击翻译按钮获取中文摘要

Bacteriophages are viruses that infect bacteria and can have a profound impact on bacterial populations. These viruses are the most abundant and diverse members of most ecosystems on Earth, having been found in every environment that supports life. The majority of the DNA sequences of newly isolated bacteriophage samples bears little or no resemblance to any sequences contained within current databases. This means that identifying new samples by looking for similar sequences is not an effective strategy. It is therefore crucial to find different approaches to characterize these viruses, which will then help is understand the critical roles that they have as members of microbial communities. This research will develop a new network-based approach for classifying viruses, thereby opening new avenues for exploring virus ecology as well as providing genomic data for evolutionary studies. This new approach will redefine how the genetic diversity of viruses is represented, making possible direct analyses of complex viral communities at higher resolution than is currently possible. Furthermore, the project will unite students from the computational and biological sciences, creating interdisciplinary teams able to carry out this research, with this training taking both in the classroom and laboratory.The project will develop a new graphical framework for the investigation of viral metagenomes, specifically aimed at putting novel viral sequences into a global context of viral genetic diversity. Because virus genomes are mosaics of genes acquired over time from multiple sources, a 'gene-centric' rather than a 'genome-centric' approach to classify the sequences in viromes is taken. In addition to capturing the combinatorial structure of virus genomes, this approach is robust to variation in genome and contig size that can arise from diverse datasets. Through the development of novel algorithms and metrics, evolutionary and ecological signals in the data will be integrated to inform the network's structure. The network will also be used to test hypotheses driven from the data, in particular the signals of viral host-range. For more information about the project visit the PI's lab website at http://www.putonti-lab.com.