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RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles

Overview of attention for article published in BMC Genomics, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

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Citations

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31 Dimensions

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89 Mendeley
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1 CiteULike
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Title
RNA sequencing of transcriptomes in human brain regions: protein-coding and non-coding RNAs, isoforms and alleles
Published in
BMC Genomics, November 2015
DOI 10.1186/s12864-015-2207-8
Pubmed ID
Authors

Amy Webb, Audrey C. Papp, Amanda Curtis, Leslie C. Newman, Maciej Pietrzak, Michal Seweryn, Samuel K. Handelman, Grzegorz A. Rempala, Daqing Wang, Erica Graziosa, Rachel F. Tyndale, Caryn Lerman, John R. Kelsoe, Deborah C. Mash, Wolfgang Sadee

Abstract

We used RNA sequencing to analyze transcript profiles of ten autopsy brain regions from ten subjects. RNA sequencing techniques were designed to detect both coding and non-coding RNA, splice isoform composition, and allelic expression. Brain regions were selected from five subjects with a documented history of smoking and five non-smokers. Paired-end RNA sequencing was performed on SOLiD instruments to a depth of >40 million reads, using linearly amplified, ribosomally depleted RNA. Sequencing libraries were prepared with both poly-dT and random hexamer primers to detect all RNA classes, including long non-coding (lncRNA), intronic and intergenic transcripts, and transcripts lacking poly-A tails, providing additional data not previously available. The study was designed to generate a database of the complete transcriptomes in brain region for gene network analyses and discovery of regulatory variants. Of 20,318 protein coding and 18,080 lncRNA genes annotated from GENCODE and lncipedia, 12 thousand protein coding and 2 thousand lncRNA transcripts were detectable at a conservative threshold. Of the aligned reads, 52 % were exonic, 34 % intronic and 14 % intergenic. A majority of protein coding genes (65 %) was expressed in all regions, whereas ncRNAs displayed a more restricted distribution. Profiles of RNA isoforms varied across brain regions and subjects at multiple gene loci, with neurexin 3 (NRXN3) a prominent example. Allelic RNA ratios deviating from unity were identified in > 400 genes, detectable in both protein-coding and non-coding genes, indicating the presence of cis-acting regulatory variants. Mathematical modeling was used to identify RNAs stably expressed in all brain regions (serving as potential markers for normalizing expression levels), linked to basic cellular functions. An initial analysis of differential expression analysis between smokers and nonsmokers implicated a number of genes, several previously associated with nicotine exposure. RNA sequencing identifies distinct and consistent differences in gene expression between brain regions, with non-coding RNA displaying greater diversity between brain regions than mRNAs. Numerous RNAs exhibit robust allele selective expression, proving a means for discovery of cis-acting regulatory factors with potential clinical relevance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
United Kingdom 1 1%
Canada 1 1%
Iran, Islamic Republic of 1 1%
United States 1 1%
Unknown 84 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 31%
Researcher 14 16%
Student > Bachelor 9 10%
Student > Postgraduate 7 8%
Student > Master 7 8%
Other 13 15%
Unknown 11 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 28%
Biochemistry, Genetics and Molecular Biology 19 21%
Neuroscience 10 11%
Engineering 5 6%
Medicine and Dentistry 5 6%
Other 10 11%
Unknown 15 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 February 2024.
All research outputs
#4,556,441
of 25,460,914 outputs
Outputs from BMC Genomics
#1,690
of 11,268 outputs
Outputs of similar age
#68,055
of 393,885 outputs
Outputs of similar age from BMC Genomics
#47
of 390 outputs
Altmetric has tracked 25,460,914 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,268 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 84% 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 393,885 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 82% of its contemporaries.
We're also able to compare this research output to 390 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.