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Next-generation mapping: a novel approach for detection of pathogenic structural variants with a potential utility in clinical diagnosis

Overview of attention for article published in Genome Medicine, October 2017
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

news
5 news outlets
blogs
1 blog
twitter
17 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
68 Dimensions

Readers on

mendeley
107 Mendeley
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Title
Next-generation mapping: a novel approach for detection of pathogenic structural variants with a potential utility in clinical diagnosis
Published in
Genome Medicine, October 2017
DOI 10.1186/s13073-017-0479-0
Pubmed ID
Authors

Hayk Barseghyan, Wilson Tang, Richard T. Wang, Miguel Almalvez, Eva Segura, Matthew S. Bramble, Allen Lipson, Emilie D. Douine, Hane Lee, Emmanuèle C. Délot, Stanley F. Nelson, Eric Vilain

Abstract

Massively parallel DNA sequencing, such as exome sequencing, has become a routine clinical procedure to identify pathogenic variants responsible for a patient's phenotype. Exome sequencing has the capability of reliably identifying inherited and de novo single-nucleotide variants, small insertions, and deletions. However, due to the use of 100-300-bp fragment reads, this platform is not well powered to sensitively identify moderate to large structural variants (SV), such as insertions, deletions, inversions, and translocations. To overcome these limitations, we used next-generation mapping (NGM) to image high molecular weight double-stranded DNA molecules (megabase size) with fluorescent tags in nanochannel arrays for de novo genome assembly. We investigated the capacity of this NGM platform to identify pathogenic SV in a series of patients diagnosed with Duchenne muscular dystrophy (DMD), due to large deletions, insertion, and inversion involving the DMD gene. We identified deletion, duplication, and inversion breakpoints within DMD. The sizes of deletions were in the range of 45-250 Kbp, whereas the one identified insertion was approximately 13 Kbp in size. This method refined the location of the break points within introns for cases with deletions compared to current polymerase chain reaction (PCR)-based clinical techniques. Heterozygous SV were detected in the known carrier mothers of the DMD patients, demonstrating the ability of the method to ascertain carrier status for large SV. The method was also able to identify a 5.1-Mbp inversion involving the DMD gene, previously identified by RNA sequencing. We showed the ability of NGM technology to detect pathogenic structural variants otherwise missed by PCR-based techniques or chromosomal microarrays. NGM is poised to become a new tool in the clinical genetic diagnostic strategy and research due to its ability to sensitively identify large genomic variations.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 107 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 21%
Researcher 22 21%
Student > Master 17 16%
Student > Bachelor 8 7%
Student > Doctoral Student 6 6%
Other 14 13%
Unknown 17 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 39 36%
Agricultural and Biological Sciences 19 18%
Medicine and Dentistry 13 12%
Computer Science 4 4%
Engineering 2 2%
Other 7 7%
Unknown 23 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 53. 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 22 February 2021.
All research outputs
#578,504
of 20,340,823 outputs
Outputs from Genome Medicine
#107
of 1,318 outputs
Outputs of similar age
#16,706
of 338,184 outputs
Outputs of similar age from Genome Medicine
#15
of 99 outputs
Altmetric has tracked 20,340,823 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,318 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.4. This one has done particularly well, scoring higher than 91% 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 338,184 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 95% 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 84% of its contemporaries.