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Biomarker discovery: quantification of microRNAs and other small non-coding RNAs using next generation sequencing

Overview of attention for article published in BMC Medical Genomics, July 2015
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

blogs
1 blog
twitter
11 tweeters

Citations

dimensions_citation
60 Dimensions

Readers on

mendeley
154 Mendeley
citeulike
1 CiteULike
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Title
Biomarker discovery: quantification of microRNAs and other small non-coding RNAs using next generation sequencing
Published in
BMC Medical Genomics, July 2015
DOI 10.1186/s12920-015-0109-x
Pubmed ID
Authors

Juan Pablo Lopez, Alpha Diallo, Cristiana Cruceanu, Laura M. Fiori, Sylvie Laboissiere, Isabelle Guillet, Joelle Fontaine, Jiannis Ragoussis, Vladimir Benes, Gustavo Turecki, Carl Ernst

Abstract

Small ncRNAs (sncRNAs) offer great hope as biomarkers of disease and response to treatment. This has been highlighted in the context of several medical conditions such as cancer, liver disease, cardiovascular disease, and central nervous system disorders, among many others. Here we assessed several steps involved in the development of an ncRNA biomarker discovery pipeline, ranging from sample preparation to bioinformatic processing of small RNA sequencing data. A total of 45 biological samples were included in the present study. All libraries were prepared using the Illumina TruSeq Small RNA protocol and sequenced using the HiSeq2500 or MiSeq Illumina sequencers. Small RNA sequencing data was validated using qRT-PCR. At each stage, we evaluated the pros and cons of different techniques that may be suitable for different experimental designs. Evaluation methods included quality of data output in relation to hands-on laboratory time, cost, and efficiency of processing. Our results show that good quality sequencing libraries can be prepared from small amounts of total RNA and that varying degradation levels in the samples do not have a significant effect on the overall quantification of sncRNAs via NGS. In addition, we describe the strengths and limitations of three commercially available library preparation methods: (1) Novex TBE PAGE gel; (2) Pippin Prep automated gel system; and (3) AMPure XP beads. We describe our bioinformatics pipeline, provide recommendations for sequencing coverage, and describe in detail the expression and distribution of all sncRNAs in four human tissues: whole-blood, brain, heart and liver. Ultimately this study provides tools and outcome metrics that will aid researchers and clinicians in choosing an appropriate and effective high-throughput sequencing quantification method for various study designs, and overall generating valuable information that can contribute to our understanding of small ncRNAs as potential biomarkers and mediators of biological functions and disease.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
United Kingdom 1 <1%
South Africa 1 <1%
Ireland 1 <1%
Canada 1 <1%
Denmark 1 <1%
Switzerland 1 <1%
Luxembourg 1 <1%
Unknown 145 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 25%
Student > Ph. D. Student 28 18%
Student > Master 18 12%
Student > Bachelor 15 10%
Other 11 7%
Other 21 14%
Unknown 23 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 30%
Biochemistry, Genetics and Molecular Biology 31 20%
Medicine and Dentistry 19 12%
Chemistry 7 5%
Neuroscience 5 3%
Other 15 10%
Unknown 31 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 June 2016.
All research outputs
#1,617,640
of 15,456,279 outputs
Outputs from BMC Medical Genomics
#67
of 803 outputs
Outputs of similar age
#29,096
of 233,941 outputs
Outputs of similar age from BMC Medical Genomics
#1
of 7 outputs
Altmetric has tracked 15,456,279 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 803 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 91% 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 233,941 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 87% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them