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Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas

Overview of attention for article published in Scientific Data, March 2017
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Title
Comprehensive RNA-seq transcriptomic profiling in the malignant progression of gliomas
Published in
Scientific Data, March 2017
DOI 10.1038/sdata.2017.24
Pubmed ID
Authors

Zheng Zhao, Fanlin Meng, Wen Wang, Zheng Wang, Chuanbao Zhang, Tao Jiang

Abstract

Gliomas are the most common and lethal intracranial tumours. RNA sequencing technologies and advanced data analyses recently enabled the characterization of transcriptomic information, including protein-coding gene expression, non-coding gene expression, alternative splicing, and fusion gene detection, to facilitate detection of diseases and altered phenotypes. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to delineate comprehensive transcriptome profiling in the malignant progression of human gliomas. Three hundred twenty five gliomas with different grades were collected over the past twelve years. Using the Illumina HiSeq 2,000 system, over 92 million high quality 101-bp paired-end reads were generated per sample, yielding a total of 30 billion reads. This comprehensive dataset will be useful to deepen the comprehensive understanding of gliomas, providing an opportunity to generate new therapies, diagnoses, and preventive strategies.

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X Demographics

The data shown below were collected from the profiles of 5 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 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 2 2%
Unknown 92 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 19%
Researcher 13 14%
Student > Bachelor 12 13%
Student > Master 11 12%
Student > Postgraduate 5 5%
Other 10 11%
Unknown 25 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 26 28%
Agricultural and Biological Sciences 11 12%
Medicine and Dentistry 10 11%
Immunology and Microbiology 4 4%
Chemistry 3 3%
Other 11 12%
Unknown 29 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 March 2017.
All research outputs
#13,468,709
of 22,959,818 outputs
Outputs from Scientific Data
#1,847
of 2,490 outputs
Outputs of similar age
#158,133
of 307,966 outputs
Outputs of similar age from Scientific Data
#46
of 59 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,490 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.5. This one is in the 25th percentile – i.e., 25% 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 307,966 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.