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Genome annotation for clinical genomic diagnostics: strengths and weaknesses

Overview of attention for article published in Genome Medicine, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

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32 X users
facebook
2 Facebook pages
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6 Wikipedia pages

Readers on

mendeley
159 Mendeley
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3 CiteULike
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Title
Genome annotation for clinical genomic diagnostics: strengths and weaknesses
Published in
Genome Medicine, May 2017
DOI 10.1186/s13073-017-0441-1
Pubmed ID
Authors

Charles A. Steward, Alasdair P. J. Parker, Berge A. Minassian, Sanjay M. Sisodiya, Adam Frankish, Jennifer Harrow

Abstract

The Human Genome Project and advances in DNA sequencing technologies have revolutionized the identification of genetic disorders through the use of clinical exome sequencing. However, in a considerable number of patients, the genetic basis remains unclear. As clinicians begin to consider whole-genome sequencing, an understanding of the processes and tools involved and the factors to consider in the annotation of the structure and function of genomic elements that might influence variant identification is crucial. Here, we discuss and illustrate the strengths and weaknesses of approaches for the annotation and classification of important elements of protein-coding genes, other genomic elements such as pseudogenes and the non-coding genome, comparative-genomic approaches for inferring gene function, and new technologies for aiding genome annotation, as a practical guide for clinicians when considering pathogenic sequence variation. Complete and accurate annotation of structure and function of genome features has the potential to reduce both false-negative (from missing annotation) and false-positive (from incorrect annotation) errors in causal variant identification in exome and genome sequences. Re-analysis of unsolved cases will be necessary as newer technology improves genome annotation, potentially improving the rate of diagnosis.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 159 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Unknown 157 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 20%
Student > Master 24 15%
Student > Bachelor 19 12%
Student > Ph. D. Student 19 12%
Student > Postgraduate 10 6%
Other 18 11%
Unknown 37 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 54 34%
Agricultural and Biological Sciences 28 18%
Medicine and Dentistry 10 6%
Computer Science 9 6%
Nursing and Health Professions 3 2%
Other 15 9%
Unknown 40 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. 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 16 December 2021.
All research outputs
#1,699,217
of 25,837,817 outputs
Outputs from Genome Medicine
#366
of 1,611 outputs
Outputs of similar age
#32,040
of 331,828 outputs
Outputs of similar age from Genome Medicine
#7
of 30 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,611 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one has done well, scoring higher than 77% 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 331,828 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 90% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.