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Development and validation of a new high-throughput method to investigate the clonality of HTLV-1-infected cells based on provirus integration sites

Overview of attention for article published in Genome Medicine, June 2014
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3 X users

Citations

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69 Mendeley
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Title
Development and validation of a new high-throughput method to investigate the clonality of HTLV-1-infected cells based on provirus integration sites
Published in
Genome Medicine, June 2014
DOI 10.1186/gm568
Pubmed ID
Authors

Sanaz Firouzi, Yosvany López, Yutaka Suzuki, Kenta Nakai, Sumio Sugano, Tadanori Yamochi, Toshiki Watanabe

Abstract

Transformation and clonal proliferation of T-cells infected with human T-cell leukemia virus type-I (HTLV-1) cause adult T-cell leukemia. We took advantage of next-generation sequencing technology to develop and internally validate a new methodology for isolating integration sites and estimating the number of cells in each HTLV-1-infected clone (clone size). Initial analysis was performed with DNA samples from infected individuals. We then used appropriate controls with known integration sites and clonality status to confirm the accuracy of our system, which indeed had the least errors among the currently available techniques. Results suggest potential clinical and biological applications of the new method.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 2 3%
Germany 1 1%
Brazil 1 1%
United Kingdom 1 1%
Denmark 1 1%
Unknown 63 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 29%
Student > Ph. D. Student 19 28%
Student > Bachelor 6 9%
Student > Master 5 7%
Student > Doctoral Student 3 4%
Other 9 13%
Unknown 7 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 35%
Biochemistry, Genetics and Molecular Biology 15 22%
Medicine and Dentistry 10 14%
Immunology and Microbiology 3 4%
Engineering 3 4%
Other 6 9%
Unknown 8 12%
Attention Score in Context

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 03 November 2019.
All research outputs
#14,783,688
of 25,373,627 outputs
Outputs from Genome Medicine
#1,333
of 1,585 outputs
Outputs of similar age
#119,543
of 242,574 outputs
Outputs of similar age from Genome Medicine
#15
of 26 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 15th percentile – i.e., 15% 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 242,574 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 50% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.