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Regulatory network changes between cell lines and their tissues of origin

Overview of attention for article published in BMC Genomics, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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41 X users
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1 Google+ user

Citations

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90 Mendeley
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Title
Regulatory network changes between cell lines and their tissues of origin
Published in
BMC Genomics, September 2017
DOI 10.1186/s12864-017-4111-x
Pubmed ID
Authors

Camila M. Lopes-Ramos, Joseph N. Paulson, Cho-Yi Chen, Marieke L. Kuijjer, Maud Fagny, John Platig, Abhijeet R. Sonawane, Dawn L. DeMeo, John Quackenbush, Kimberly Glass

Abstract

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

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X Demographics

The data shown below were collected from the profiles of 41 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 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 27%
Researcher 17 19%
Student > Master 8 9%
Student > Bachelor 8 9%
Professor > Associate Professor 6 7%
Other 14 16%
Unknown 13 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 42%
Agricultural and Biological Sciences 17 19%
Medicine and Dentistry 8 9%
Computer Science 4 4%
Engineering 2 2%
Other 6 7%
Unknown 15 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 08 March 2019.
All research outputs
#1,661,894
of 25,656,290 outputs
Outputs from BMC Genomics
#314
of 11,298 outputs
Outputs of similar age
#31,754
of 324,311 outputs
Outputs of similar age from BMC Genomics
#4
of 220 outputs
Altmetric has tracked 25,656,290 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,298 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 97% 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 324,311 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 220 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 98% of its contemporaries.