↓ Skip to main content

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework

Overview of attention for article published in Genome Medicine, December 2017
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
50 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
113 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework
Published in
Genome Medicine, December 2017
DOI 10.1186/s13073-017-0509-y
Pubmed ID
Authors

Gustavo Glusman, Peter W. Rose, Andreas Prlić, Jennifer Dougherty, José M. Duarte, Andrew S. Hoffman, Geoffrey J. Barton, Emøke Bendixen, Timothy Bergquist, Christian Bock, Elizabeth Brunk, Marija Buljan, Stephen K. Burley, Binghuang Cai, Hannah Carter, JianJiong Gao, Adam Godzik, Michael Heuer, Michael Hicks, Thomas Hrabe, Rachel Karchin, Julia Koehler Leman, Lydie Lane, David L. Masica, Sean D. Mooney, John Moult, Gilbert S. Omenn, Frances Pearl, Vikas Pejaver, Sheila M. Reynolds, Ariel Rokem, Torsten Schwede, Sicheng Song, Hagen Tilgner, Yana Valasatava, Yang Zhang, Eric W. Deutsch

Abstract

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

Twitter Demographics

The data shown below were collected from the profiles of 50 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 113 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 20%
Student > Ph. D. Student 20 18%
Student > Bachelor 15 13%
Student > Master 12 11%
Student > Doctoral Student 7 6%
Other 17 15%
Unknown 19 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 40 35%
Agricultural and Biological Sciences 22 19%
Computer Science 9 8%
Medicine and Dentistry 6 5%
Immunology and Microbiology 4 4%
Other 10 9%
Unknown 22 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 33. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 June 2020.
All research outputs
#789,676
of 18,034,577 outputs
Outputs from Genome Medicine
#162
of 1,212 outputs
Outputs of similar age
#27,160
of 418,555 outputs
Outputs of similar age from Genome Medicine
#24
of 99 outputs
Altmetric has tracked 18,034,577 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,212 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.5. This one has done well, scoring higher than 86% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 418,555 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.