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Differential expression analysis of human endogenous retroviruses based on ENCODE RNA-seq data

Overview of attention for article published in BMC Medical Genomics, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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Title
Differential expression analysis of human endogenous retroviruses based on ENCODE RNA-seq data
Published in
BMC Medical Genomics, November 2015
DOI 10.1186/s12920-015-0146-5
Pubmed ID
Authors

Kerstin Haase, Anja Mösch, Dmitrij Frishman

Abstract

Human endogenous retroviruses (HERVs) are flanked by long terminal repeats (LTRs), which possess promoter activity and can therefore influence the expression of neighboring genes. HERV involvement in different types of cancer has already been thoroughly documented. However, so far there has been no systematic study of HERV expression patterns in a multitude of cell types in health and disease. In particular, the publication of the comprehensive ENCODE dataset has already facilitated many gene expression studies, but none so far focusing exclusively on HERVs. We present a comprehensive differential analysis of HERV expression based on ENCODE Tier 1 and Tier 2 RNA-seq data produced by Cold Spring Harbor Laboratories and the California Institute of Technology. This analysis was conducted for individual HERV loci and for entire HERV families in twelve different cell lines, of which six correspond to the normal condition and the other six represent cancer cell types. Although the principal component analysis revealed that the two groups of cells show distinguishable expression patterns, we were not able to link these differences to one or multiple particular HERV families. Two samples exhibit expression patterns, which are not similar to the corresponding cell lines of the other producing lab. Instead they show signs of cancer formation and expression of the pluripotency marker HERVH, despite being classified as a normal cell line and a differentiated cell, respectively. Our study demonstrates that ENCODE data are generally comparable between the different contributing labs and that the analysis of HERV elements can provide novel insights into differentiation and disease state of a cell that are easily overlooked when focusing on protein-coding genes. Our findings hint at a change in HERV expression during cancerogenesis.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 1%
Japan 1 1%
Unknown 68 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 24%
Researcher 15 21%
Student > Master 9 13%
Other 4 6%
Student > Bachelor 4 6%
Other 9 13%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 36%
Biochemistry, Genetics and Molecular Biology 19 27%
Medicine and Dentistry 6 9%
Computer Science 4 6%
Immunology and Microbiology 3 4%
Other 1 1%
Unknown 12 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 13 September 2023.
All research outputs
#5,315,603
of 25,654,806 outputs
Outputs from BMC Medical Genomics
#350
of 2,455 outputs
Outputs of similar age
#66,566
of 297,068 outputs
Outputs of similar age from BMC Medical Genomics
#8
of 56 outputs
Altmetric has tracked 25,654,806 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,455 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 85% 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 297,068 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 77% of its contemporaries.
We're also able to compare this research output to 56 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.