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Deciphering the heterogeneity in DNA methylation patterns during stem cell differentiation and reprogramming

Overview of attention for article published in BMC Genomics, November 2014
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  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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60 Mendeley
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Title
Deciphering the heterogeneity in DNA methylation patterns during stem cell differentiation and reprogramming
Published in
BMC Genomics, November 2014
DOI 10.1186/1471-2164-15-978
Pubmed ID
Authors

Xiaojian Shao, Cuiyun Zhang, Ming-An Sun, Xuemei Lu, Hehuang Xie

Abstract

Human induced pluripotent stem cells (iPSCs) have a wide range of applications throughout the fields of basic research, disease modeling and drug screening. Epigenetic instable iPSCs with aberrant DNA methylation may divide and differentiate into cancer cells. Unfortunately, little effort has been taken to compare the epigenetic variation in iPSCs with that in differentiated cells. Here, we developed an analytical procedure to decipher the DNA methylation heterogeneity of mixed cells and further exploited it to quantitatively assess the DNA methylation variation in the methylomes of adipose-derived stem cells (ADS), mature adipocytes differentiated from ADS cells (ADS-adipose) and iPSCs reprogrammed from ADS cells (ADS-iPSCs).

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X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 59 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 33%
Researcher 10 17%
Student > Doctoral Student 6 10%
Student > Bachelor 5 8%
Student > Master 5 8%
Other 9 15%
Unknown 5 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 35%
Biochemistry, Genetics and Molecular Biology 15 25%
Computer Science 7 12%
Engineering 3 5%
Nursing and Health Professions 1 2%
Other 6 10%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 09 December 2014.
All research outputs
#14,638,545
of 23,881,329 outputs
Outputs from BMC Genomics
#5,518
of 10,793 outputs
Outputs of similar age
#193,032
of 368,046 outputs
Outputs of similar age from BMC Genomics
#116
of 263 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,793 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 368,046 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 263 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.