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Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer

Overview of attention for article published in BMC Bioinformatics, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
14 tweeters

Citations

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12 Dimensions

Readers on

mendeley
36 Mendeley
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Title
Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer
Published in
BMC Bioinformatics, May 2016
DOI 10.1186/s12859-016-1085-7
Pubmed ID
Authors

Liangcai Zhang, Ying Yuan, Karen H. Lu, Li Zhang

Abstract

Genomic regions with recurrent DNA copy number variations (CNVs) are generally believed to encode oncogenes and tumor suppressor genes (TSGs) that drive cancer growth. However, it remains a challenge to delineate the key cancer driver genes from the regions encoding a large number of genes. In this study, we developed a new approach to CNV analysis based on spectral decomposition of CNV profiles into focal CNVs and broad CNVs. We performed an analysis of CNV data of 587 serous ovarian cancer samples on multiple platforms. We identified a number of novel focal regions, such as focal gain of ESR1, focal loss of LSAMP, prognostic site at 3q26.2 and losses of sub-telomere regions in multiple chromosomes. Furthermore, we performed network modularity analysis to examine the relationships among genes encoded in the focal CNV regions. Our results also showed that the recurrent focal gains were significantly associated with the known oncogenes and recurrent losses associated with TSGs and the CNVs had a greater effect on the mRNA expression of the driver genes than that of the non-driver genes. Our results demonstrate that spectral decomposition of CNV profiles offers a new way of understanding the role of CNVs in cancer.

Twitter Demographics

The data shown below were collected from the profiles of 14 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 28%
Student > Master 6 17%
Researcher 5 14%
Student > Bachelor 5 14%
Other 3 8%
Other 2 6%
Unknown 5 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 33%
Agricultural and Biological Sciences 8 22%
Medicine and Dentistry 6 17%
Computer Science 3 8%
Mathematics 1 3%
Other 0 0%
Unknown 6 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 22 June 2016.
All research outputs
#3,304,694
of 19,192,351 outputs
Outputs from BMC Bioinformatics
#1,365
of 6,542 outputs
Outputs of similar age
#57,069
of 276,311 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 22 outputs
Altmetric has tracked 19,192,351 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,542 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done well, scoring higher than 79% 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 276,311 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.