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The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia

Overview of attention for article published in BMC Genomics, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters

Citations

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29 Dimensions

Readers on

mendeley
84 Mendeley
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1 CiteULike
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Title
The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia
Published in
BMC Genomics, August 2017
DOI 10.1186/s12864-017-4039-1
Pubmed ID
Authors

Ashwini Kumar, Matti Kankainen, Alun Parsons, Olli Kallioniemi, Pirkko Mattila, Caroline A. Heckman

Abstract

RNA sequencing (RNA-seq) has become an indispensable tool to identify disease associated transcriptional profiles and determine the molecular underpinnings of diseases. However, the broad adaptation of the methodology into the clinic is still hampered by inconsistent results from different RNA-seq protocols and involves further evaluation of its analytical reliability using patient samples. Here, we applied two commonly used RNA-seq library preparation protocols to samples from acute leukemia patients to understand how poly-A-tailed mRNA selection (PA) and ribo-depletion (RD) based RNA-seq library preparation protocols affect gene fusion detection, variant calling, and gene expression profiling. Overall, the protocols produced similar results with consistent outcomes. Nevertheless, the PA protocol was more efficient in quantifying expression of leukemia marker genes and showed better performance in the expression-based classification of leukemia. Independent qRT-PCR experiments verified that the PA protocol better represented total RNA compared to the RD protocol. In contrast, the RD protocol detected a higher number of non-coding RNA features and had better alignment efficiency. The RD protocol also recovered more known fusion-gene events, although variability was seen in fusion gene predictions. The overall findings provide a framework for the use of RNA-seq in a precision medicine setting with limited number of samples and suggest that selection of the library preparation protocol should be based on the objectives of the analysis.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 16 19%
Student > Master 10 12%
Student > Bachelor 9 11%
Student > Postgraduate 7 8%
Other 12 14%
Unknown 11 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 30 36%
Agricultural and Biological Sciences 15 18%
Medicine and Dentistry 9 11%
Neuroscience 4 5%
Engineering 3 4%
Other 8 10%
Unknown 15 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 04 September 2017.
All research outputs
#2,289,850
of 17,365,229 outputs
Outputs from BMC Genomics
#971
of 9,281 outputs
Outputs of similar age
#51,733
of 280,076 outputs
Outputs of similar age from BMC Genomics
#4
of 17 outputs
Altmetric has tracked 17,365,229 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,281 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 89% 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 280,076 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 81% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.