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Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization

Overview of attention for article published in Human Genomics, July 2017
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Title
Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization
Published in
Human Genomics, July 2017
DOI 10.1186/s40246-017-0112-8
Pubmed ID
Authors

Amir Farmanbar, Sanaz Firouzi, Wojciech Makałowski, Masako Iwanaga, Kaoru Uchimaru, Atae Utsunomiya, Toshiki Watanabe, Kenta Nakai

Abstract

Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated. We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples. From four size categories analyzed, we found that big clones (B; 513-2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition. This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 25%
Student > Bachelor 2 17%
Student > Ph. D. Student 1 8%
Other 1 8%
Student > Master 1 8%
Other 3 25%
Unknown 1 8%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Engineering 3 25%
Immunology and Microbiology 2 17%
Medicine and Dentistry 2 17%
Computer Science 1 8%
Other 0 0%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 March 2020.
All research outputs
#9,710,535
of 15,257,867 outputs
Outputs from Human Genomics
#206
of 301 outputs
Outputs of similar age
#162,432
of 279,242 outputs
Outputs of similar age from Human Genomics
#2
of 3 outputs
Altmetric has tracked 15,257,867 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 301 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one is in the 23rd percentile – i.e., 23% 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 279,242 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.