↓ Skip to main content

In-depth characterization of the microRNA transcriptome in a leukemia progression model

Overview of attention for article published in Genome Research, November 2008
Altmetric Badge

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 (75th percentile)

Mentioned by

3 patents


144 Dimensions

Readers on

149 Mendeley
8 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
In-depth characterization of the microRNA transcriptome in a leukemia progression model
Published in
Genome Research, November 2008
DOI 10.1101/gr.077578.108
Pubmed ID

Florian Kuchenbauer, Ryan D. Morin, Bob Argiropoulos, Oleh I. Petriv, Malachi Griffith, Michael Heuser, Eric Yung, Jessica Piper, Allen Delaney, Anna-Liisa Prabhu, Yongjun Zhao, Helen McDonald, Thomas Zeng, Martin Hirst, Carl L. Hansen, Marco A. Marra, R. Keith Humphries


MicroRNAs (miRNAs) have been shown to play important roles in physiological as well as multiple malignant processes, including acute myeloid leukemia (AML). In an effort to gain further insight into the role of miRNAs in AML, we have applied the Illumina massively parallel sequencing platform to carry out an in-depth analysis of the miRNA transcriptome in a murine leukemia progression model. This model simulates the stepwise conversion of a myeloid progenitor cell by an engineered overexpression of the nucleoporin 98 (NUP98)-homeobox HOXD13 fusion gene (ND13), to aggressive AML inducing cells upon transduction with the oncogenic collaborator Meis1. From this data set, we identified 307 miRNA/miRNA species in the ND13 cells and 306 miRNA/miRNA species in ND13+Meis1 cells, corresponding to 223 and 219 miRNA genes. Sequence counts varied between two and 136,558, indicating a remarkable expression range between the detected miRNA species. The large number of miRNAs expressed and the nature of differential expression suggest that leukemic progression as modeled here is dictated by the repertoire of shared, but differentially expressed miRNAs. Our finding of extensive sequence variations (isomiRs) for almost all miRNA and miRNA species adds additional complexity to the miRNA transcriptome. A stringent target prediction analysis coupled with in vitro target validation revealed the potential for miRNA-mediated release of oncogenes that facilitates leukemic progression from the preleukemic to leukemia inducing state. Finally, 55 novel miRNAs species were identified in our data set, adding further complexity to the emerging world of small RNAs.

Mendeley readers

The data shown below were compiled from readership statistics for 149 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 10 7%
United Kingdom 4 3%
Canada 2 1%
Japan 1 <1%
Czech Republic 1 <1%
Spain 1 <1%
Unknown 130 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 51 34%
Student > Ph. D. Student 45 30%
Student > Master 12 8%
Professor > Associate Professor 9 6%
Student > Postgraduate 7 5%
Other 25 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 100 67%
Biochemistry, Genetics and Molecular Biology 22 15%
Medicine and Dentistry 9 6%
Unspecified 6 4%
Computer Science 3 2%
Other 9 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 November 2016.
All research outputs
of 11,097,556 outputs
Outputs from Genome Research
of 3,154 outputs
Outputs of similar age
of 265,516 outputs
Outputs of similar age from Genome Research
of 43 outputs
Altmetric has tracked 11,097,556 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,154 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has gotten more attention than average, scoring higher than 52% 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 265,516 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 75% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.