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Exome analysis of carotid body tumor

Overview of attention for article published in BMC Medical Genomics, February 2018
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Title
Exome analysis of carotid body tumor
Published in
BMC Medical Genomics, February 2018
DOI 10.1186/s12920-018-0327-0
Pubmed ID
Authors

Anastasiya V. Snezhkina, Elena N. Lukyanova, Dmitry V. Kalinin, Anatoly V. Pokrovsky, Alexey A. Dmitriev, Nadezhda V. Koroban, Elena A. Pudova, Maria S. Fedorova, Nadezhda N. Volchenko, Oleg A. Stepanov, Ekaterina A. Zhevelyuk, Sergey L. Kharitonov, Anastasiya V. Lipatova, Ivan S. Abramov, Alexander V. Golovyuk, Yegor E. Yegorov, Khava S. Vishnyakova, Alexey A. Moskalev, George S. Krasnov, Nataliya V. Melnikova, Dmitry S. Shcherbo, Marina V. Kiseleva, Andrey D. Kaprin, Boris Y. Alekseev, Andrew R. Zaretsky, Anna V. Kudryavtseva

Abstract

Carotid body tumor (CBT) is a form of head and neck paragangliomas (HNPGLs) arising at the bifurcation of carotid arteries. Paragangliomas are commonly associated with germline and somatic mutations involving at least one of more than thirty causative genes. However, the specific functionality of a number of these genes involved in the formation of paragangliomas has not yet been fully investigated. Exome library preparation was carried out using Nextera® Rapid Capture Exome Kit (Illumina, USA). Sequencing was performed on NextSeq 500 System (Illumina). Exome analysis of 52 CBTs revealed potential driver mutations (PDMs) in 21 genes: ARNT, BAP1, BRAF, BRCA1, BRCA2, CDKN2A, CSDE1, FGFR3, IDH1, KIF1B, KMT2D, MEN1, RET, SDHA, SDHB, SDHC, SDHD, SETD2, TP53BP1, TP53BP2, and TP53I13. In many samples, more than one PDM was identified. There are also 41% of samples in which we did not identify any PDM; in these cases, the formation of CBT was probably caused by the cumulative effect of several not highly pathogenic mutations. Estimation of average mutation load demonstrated 6-8 mutations per megabase (Mb). Genes with the highest mutation rate were identified. Exome analysis of 52 CBTs for the first time revealed the average mutation load for these tumors and also identified potential driver mutations as well as their frequencies and co-occurrence with the other PDMs.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Master 6 19%
Student > Ph. D. Student 3 9%
Student > Bachelor 2 6%
Professor > Associate Professor 2 6%
Other 2 6%
Unknown 11 34%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 31%
Medicine and Dentistry 6 19%
Agricultural and Biological Sciences 1 3%
Nursing and Health Professions 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 12 38%
Attention Score in Context

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 06 March 2018.
All research outputs
#20,466,701
of 23,025,074 outputs
Outputs from BMC Medical Genomics
#1,012
of 1,233 outputs
Outputs of similar age
#383,527
of 446,086 outputs
Outputs of similar age from BMC Medical Genomics
#15
of 17 outputs
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So far Altmetric has tracked 1,233 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.