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

Information

  • NSF Award
  • 1457957
Owner
  • Award Id
    1457957
  • Award Effective Date
    6/1/2015 - 9 years ago
  • Award Expiration Date
    5/31/2018 - 6 years ago
  • Award Amount
    $ 503,297.00
  • Award Instrument
    Continuing grant

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

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.<br/><br/>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.

  • Program Officer
    Peter H. McCartney
  • Min Amd Letter Date
    5/11/2015 - 9 years ago
  • Max Amd Letter Date
    6/24/2016 - 8 years ago
  • ARRA Amount

Institutions

  • Name
    University of Maryland at Baltimore
  • City
    Baltimore
  • State
    MD
  • Country
    United States
  • Address
    620 W Lexington St, 4th Floor
  • Postal Code
    212011508
  • Phone Number
    4107063559

Investigators

  • First Name
    Julie
  • Last Name
    Hotopp
  • Email Address
    jhotopp@som.umaryland.edu
  • Start Date
    5/11/2015 12:00:00 AM

Program Element

  • Text
    ADVANCES IN BIO INFORMATICS
  • Code
    1165