↓ Skip to main content

Interrogating the “unsequenceable” genomic trinucleotide repeat disorders by long-read sequencing

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

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
64 Dimensions

Readers on

mendeley
133 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
Interrogating the “unsequenceable” genomic trinucleotide repeat disorders by long-read sequencing
Published in
Genome Medicine, July 2017
DOI 10.1186/s13073-017-0456-7
Pubmed ID
Authors

Qian Liu, Peng Zhang, Depeng Wang, Weihong Gu, Kai Wang

Abstract

Microsatellite expansion, such as trinucleotide repeat expansion (TRE), is known to cause a number of genetic diseases. Sanger sequencing and next-generation short-read sequencing are unable to interrogate TRE reliably. We developed a novel algorithm called RepeatHMM to estimate repeat counts from long-read sequencing data. Evaluation on simulation data, real amplicon sequencing data on two repeat expansion disorders, and whole-genome sequencing data generated by PacBio and Oxford Nanopore technologies showed superior performance over competing approaches. We concluded that long-read sequencing coupled with RepeatHMM can estimate repeat counts on microsatellites and can interrogate the "unsequenceable" genomic trinucleotide repeat disorders.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 133 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 21%
Researcher 24 18%
Student > Master 15 11%
Student > Bachelor 13 10%
Student > Doctoral Student 8 6%
Other 23 17%
Unknown 22 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 52 39%
Agricultural and Biological Sciences 27 20%
Medicine and Dentistry 11 8%
Computer Science 5 4%
Neuroscience 4 3%
Other 8 6%
Unknown 26 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 July 2017.
All research outputs
#5,609,698
of 17,365,229 outputs
Outputs from Genome Medicine
#892
of 1,156 outputs
Outputs of similar age
#101,223
of 273,672 outputs
Outputs of similar age from Genome Medicine
#6
of 6 outputs
Altmetric has tracked 17,365,229 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,156 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.4. This one is in the 19th percentile – i.e., 19% of its peers scored the same or lower than it.
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 273,672 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.