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VAReporter: variant reporter for cancer research of massive parallel sequencing

Overview of attention for article published in BMC Genomics, May 2018
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2 tweeters

Citations

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2 Dimensions

Readers on

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29 Mendeley
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Title
VAReporter: variant reporter for cancer research of massive parallel sequencing
Published in
BMC Genomics, May 2018
DOI 10.1186/s12864-018-4468-5
Pubmed ID
Authors

Po-Jung Huang, Chi-Ching Lee, Ling-Ya Chiu, Kuo-Yang Huang, Yuan-Ming Yeh, Chia-Yu Yang, Cheng-Hsun Chiu, Petrus Tang

Abstract

High throughput sequencing technologies have been an increasingly critical aspect of precision medicine owing to a better identification of disease targets, which contributes to improved health care cost and clinical outcomes. In particular, disease-oriented targeted enrichment sequencing is becoming a widely-accepted application for diagnostic purposes, which can interrogate known diagnostic variants as well as identify novel biomarkers from panels of entire human coding exome or disease-associated genes. We introduce a workflow named VAReporter to facilitate the management of variant assessment in disease-targeted sequencing, the identification of pathogenic variants, the interpretation of biological effects and the prioritization of clinically actionable targets. State-of-art algorithms that account for mutation phenotypes are used to rank the importance of mutated genes through visual analytic strategies. We established an extensive annotation source by integrating a wide variety of biomedical databases and followed the American College of Medical Genetics and Genomics (ACMG) guidelines for interpretation and reporting of sequence variations. In summary, VAReporter is the first web server designed to provide a "one-stop" resource for individual's diagnosis and large-scale cohort studies, and is freely available at http://rnd.cgu.edu.tw/vareporter .

Twitter Demographics

The data shown below were collected from the profiles of 2 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 31%
Student > Master 4 14%
Other 3 10%
Student > Ph. D. Student 2 7%
Student > Bachelor 2 7%
Other 1 3%
Unknown 8 28%
Readers by discipline Count As %
Medicine and Dentistry 6 21%
Biochemistry, Genetics and Molecular Biology 4 14%
Agricultural and Biological Sciences 2 7%
Computer Science 2 7%
Neuroscience 2 7%
Other 5 17%
Unknown 8 28%

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 14 May 2018.
All research outputs
#10,322,453
of 12,936,827 outputs
Outputs from BMC Genomics
#5,880
of 7,598 outputs
Outputs of similar age
#202,268
of 269,941 outputs
Outputs of similar age from BMC Genomics
#12
of 20 outputs
Altmetric has tracked 12,936,827 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,598 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.