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GHOST: global hepatitis outbreak and surveillance technology

Overview of attention for article published in BMC Genomics, December 2017
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1 Google+ user

Citations

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39 Dimensions

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56 Mendeley
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Title
GHOST: global hepatitis outbreak and surveillance technology
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4268-3
Pubmed ID
Authors

Atkinson G. Longmire, Seth Sims, Inna Rytsareva, David S. Campo, Pavel Skums, Zoya Dimitrova, Sumathi Ramachandran, Magdalena Medrzycki, Hong Thai, Lilia Ganova-Raeva, Yulin Lin, Lili T. Punkova, Amanda Sue, Massimo Mirabito, Silver Wang, Robin Tracy, Victor Bolet, Thom Sukalac, Chris Lynberg, Yury Khudyakov

Abstract

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way. We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission. GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

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X Demographics

The data shown below were collected from the profiles of 2 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 56 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 16%
Student > Bachelor 7 13%
Student > Master 6 11%
Student > Ph. D. Student 3 5%
Professor > Associate Professor 3 5%
Other 9 16%
Unknown 19 34%
Readers by discipline Count As %
Medicine and Dentistry 9 16%
Biochemistry, Genetics and Molecular Biology 5 9%
Agricultural and Biological Sciences 5 9%
Nursing and Health Professions 4 7%
Computer Science 4 7%
Other 8 14%
Unknown 21 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 August 2023.
All research outputs
#15,087,955
of 25,257,066 outputs
Outputs from BMC Genomics
#5,373
of 11,206 outputs
Outputs of similar age
#231,794
of 453,085 outputs
Outputs of similar age from BMC Genomics
#111
of 228 outputs
Altmetric has tracked 25,257,066 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,206 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 49th percentile – i.e., 49% 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 453,085 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 228 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.