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Mendeley readers
Attention Score in Context
| Title |
HIVID: An efficient method to detect HBV integration using low coverage sequencing
|
|---|---|
| Published in |
Genomics, July 2013
|
| DOI | 10.1016/j.ygeno.2013.07.002 |
| Pubmed ID | |
| Authors |
Weiyang Li, Xi Zeng, Nikki P. Lee, Xiao Liu, Shengpei Chen, Bing Guo, Shang Yi, Xuehan Zhuang, Fang Chen, Guan Wang, Ronnie T. Poon, Sheung Tat Fan, Mao Mao, Yingrui Li, Songgang Li, Jun Wang, JianWang, Xun Xu, Hui Jiang, Xiuqing Zhang |
| Abstract |
We reported HIVID (high-throughput Viral Integration Detection), a novel experimental and computational method to detect the location of Hepatitis B Virus (HBV) integration breakpoints in Hepatocellular Carcinoma (HCC) genome. In this method, the fragments with HBV sequence were enriched by a set of HBV probes and then processed to high-throughput sequencing. In order to evaluate the performance of HIVID, we compared the results of HIVID with that of whole genome sequencing method (WGS) in 28 HCC tumors. We detected a total of 246 HBV integration breakpoints in HCC genome, 113 out of which were within 400bp upstream or downstream of 125 breakpoints identified by WGS method, covering 89.3% (125/140) of total breakpoints. The integration was located in the gene TERT, MLL4, and CCNE1. In addition, we discovered 133 novel breakpoints missed by WGS method, with 66.7% (10/15) of validation rate. Our study shows HIVID is a cost-effective methodology with high specificity and sensitivity to identify viral integration in human genome. |
Mendeley readers
The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Sweden | 1 | 1% |
| China | 1 | 1% |
| Unknown | 87 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 21% |
| Researcher | 16 | 18% |
| Student > Master | 13 | 14% |
| Student > Bachelor | 9 | 10% |
| Other | 6 | 7% |
| Other | 13 | 14% |
| Unknown | 15 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 31 | 34% |
| Agricultural and Biological Sciences | 16 | 18% |
| Medicine and Dentistry | 8 | 9% |
| Computer Science | 6 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 2% |
| Other | 8 | 9% |
| Unknown | 20 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 May 2022.
All research outputs
#2,201,923
of 25,373,627 outputs
Outputs from Genomics
#66
of 5,923 outputs
Outputs of similar age
#18,282
of 206,789 outputs
Outputs of similar age from Genomics
#2
of 14 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,923 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done particularly well, scoring higher than 98% 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 206,789 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 91% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.