BioGPS has become the valuable resource that it is because of the contributions from our wonderful user community. Thank you for contributing plugins, suggestions, and ideas–all of which have improved BioGPS for everyone. In order to celebrate the contributions of BioGPS users to the scientific research community, this series will feature publications and articles generated by BioGPS users. We sincerely hope you will join us in celebrating the fascinating work that YOU do.

This week, we will feature an article from the National Human Genome Center (NHGC)-Biophysics Research and Interdisciplinary Development Group (BRIDG) Development of genodynamic metrics for exploring the biophysics of DNA polymorphisms by James Lindesay, Tshela E Mason, William Hercules, and Georgia M Dunston.

Dr. Georgia M Dunston, the director at NHGC, kindly answered our inquiries for this series.

  1. Who is the team behind the work that was published in Development of genodynamic metrics for exploring the biophysics of DNA polymorphisms?.
  2. The team behind the work is the NHGC-BRIDG, comprised of geneticists/genomicists, theoretical physicists, biophysicists, bioinformaticians, and molecular biologists.

  4. What inspired the work published in Development of genodynamic metrics for exploring the biophysics of DNA polymorphisms?
    The inspiration stemmed from the desire to understand the function/biology and information dynamics of single nucleotide polymorphisms (SNPs). This led us to the generation of biophysical metrics for decoding patterns in common genomic variants. The first metric we derived was the normalized information content (NIC), which was used to develop the genomic energy unit (GEU).

  6. Please provide a brief summary of the findings reported in your article, Development of genodynamic metrics for exploring the biophysics of DNA polymorphisms.
    WWe found that the lowest GEUs correlated with highly conserved SNP haploblocks and that these haploblocks were associated with evolutionarily conserved regulatory elements and domains.

  8. How did the team learn about BioGPS?
    BioGPS was found as part of our internet search for resources that annotate gene and protein function.

  10. How did your team utilize BioGPS in this research?
    We used BioGPS as part of our bioinformatic analyses in searching for the function and/or biology of SNPs.

  12. What are some future directions for the team behind this research?
    The development of these genodynamic metrics allows us to model genome-environment interactions. This offers an innovative way of probing population diversity as an expression of whole-genome adaptation to environmental stressors. Our future directions also include the construction of information maps of the human genome. Such maps will allow us to correlate the vast amount of big data from DNA sequencing with the structure of genomic information.

Thanks again to Dr. Georgia M Dunston for taking the time to answer our questions. Click here to read their fascinating article. Have a look because these awesome researchers have made their compelling research open access. Not only can you read the whole exciting article for free, you may even find their approach useful for your own research!

Used BioGPS and cited it in your publication? Let us know! We would love to feature YOUR work, no matter how long ago it was published. BioGPS Featured Article Series only started recently, but we know your contributions to science is ongoing.