ABI: Development: Cloud-based Identification and Visualization of Lateral Gene Transfers in Genome Data

ABI：开发：基因组数据中横向基因转移的基于云的识别和可视化

• 批准号: 1457957
• 负责人: Julie Hotopp
• 金额: $ 70.53万
• 依托单位: University of Maryland at Baltimore
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457957&HistoricalAwards=false
• 关键词: ABI; Development; Cloud; based; Identification

All genomes accumulate mutations that are both beneficial and detrimental to the organism. For example, mutations occur upon exposure to sun in the genome of skin cells that can ultimately lead to the development of skin cancer. Mutations range in size from single base pair alterations to massive insertions and deletions that can span over a million base pairs. The best understood mutations are those that involve alteration, insertion, or deletion of a single base pair, where there are numerous tools for identifying and validating such changes. Yet in many organisms, it is increasingly appreciated that large, even massive, insertions of DNA can occur from other organisms, termed lateral gene transfer, that have the potential to have a profound effect on the organism, either detrimental or beneficial. For example, large insertional mutations led to the transition of endosymbionts to organelles like mitochondria and chloroplasts. This project seeks to improve tools previously developed to identify such lateral gene transfers from genome sequencing data, and to make these tools available to the research community after ensuring that they are more robust and user friendly. The team has already used these tools to identify integration of bacterial DNA into numerous animal genomes, including into human somatic cell genomes of individuals with cancer, where such mutations may be oncogenic. Making the tools available to more scientists should increase the understanding of the occurrence and importance of such mutations in all organisms. In addition, this proposal seeks to develop YouTube whiteboard videos to educate the general public about these mutations, genomics, and the tools developed in this proposal. The first of these videos can be found at: https://www.youtube.com/watch?v=PZG4qjVjJ70.Lateral gene transfer (LGT, synonymous with horizontal gene transfer or HGT) is the movement of DNA between diverse organisms. It is one form of insertional mutagenesis and can be advantageous or deleterious. In bacteria, LGT has been implicated in antibiotic resistance, pathogenesis, and bioremediation. While the greatest focus has been on bacteria, it has become increasingly clear that it occurs in eukaryotes as well. For example, our research focuses on interdomain LGT between bacteria and Metazoans, which is increasingly described. This has led to the hypothesis that that LGT may have contributed more to the evolution of phenotypes in eukaryotes than previously assumed. However, there continue to be barriers to detecting LGT, particularly in Metazoan genomes. The goal of this project is to develop a bioinformatics resource in the form of a virtual machine to aid in the detection of LGT. This tool can be used to detect LGT between any suspected donor-recipient pair, including endosymbiont-host and organelle-host pairs as well as organelle-organelle, bacteria-bacteria, and viral-host, to name just a few. Such a tool could also be used by metagenomics researchers teasing apart LGT from assembly artifacts, geneticists trying to identify the integration site of a transposon following a selective screen, and for identification of integration sites for known mobile elements. Essentially, this tool can be used to detect the integration of novel DNA in any genome with some knowledge about the donor and/or recipient genome. Most sequence analysis tools have focused on SNPs or small insertions/deletions. The development of bioinformatics tools to detect larger insertions is an area that has been under-served and currently lacks robust bioinformatics tools. This proposal aims to meet those needs. Ongoing results of the project can be found at: http://lgt.igs.umaryland.edu/nsf_abi.