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RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking

Overview of attention for article published in BMC Medical Genomics, October 2017
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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Title
RNA sequencing identifies novel non-coding RNA and exon-specific effects associated with cigarette smoking
Published in
BMC Medical Genomics, October 2017
DOI 10.1186/s12920-017-0295-9
Pubmed ID
Authors

Margaret M. Parker, Robert P. Chase, Andrew Lamb, Alejandro Reyes, Aabida Saferali, Jeong H. Yun, Blanca E. Himes, Edwin K. Silverman, Craig P. Hersh, Peter J. Castaldi

Abstract

Cigarette smoking is the leading modifiable risk factor for disease and death worldwide. Previous studies quantifying gene-level expression have documented the effect of smoking on mRNA levels. Using RNA sequencing, it is possible to analyze the impact of smoking on complex regulatory phenomena (e.g. alternative splicing, differential isoform usage) leading to a more detailed understanding of the biology underlying smoking-related disease. We used whole-blood RNA sequencing to describe gene and exon-level expression differences between 229 current and 286 former smokers in the COPDGene study. We performed differential gene expression and differential exon usage analyses using the voom/limma and DEXseq R packages. Samples from current and former smokers were compared while controlling for age, gender, race, lifetime smoke exposure, cell counts, and technical covariates. At an adjusted p-value <0.05, 171 genes were differentially expressed between current and former smokers. Differentially expressed genes included 7 long non-coding RNAs that have not been previously associated with smoking: LINC00599, LINC01362, LINC00824, LINC01624, RP11-563D10.1, RP11-98G13.1, AC004791.2. Secondary analysis of acute smoking (having smoked within 2-h) revealed 5 of the 171 smoking genes demonstrated an acute response above the baseline effect of chronic smoking. Exon-level analyses identified 9 exons from 8 genes with significant differential usage by smoking status, suggesting smoking-induced changes in isoform expression. Transcriptomic changes at the gene and exon levels from whole blood can refine our understanding of the molecular mechanisms underlying the response to smoking.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 20%
Researcher 10 17%
Student > Master 6 10%
Student > Bachelor 4 7%
Professor > Associate Professor 2 3%
Other 4 7%
Unknown 22 37%
Readers by discipline Count As %
Medicine and Dentistry 12 20%
Biochemistry, Genetics and Molecular Biology 11 18%
Agricultural and Biological Sciences 4 7%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Computer Science 2 3%
Other 6 10%
Unknown 22 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 19 October 2017.
All research outputs
#6,438,306
of 23,005,189 outputs
Outputs from BMC Medical Genomics
#288
of 1,230 outputs
Outputs of similar age
#104,947
of 323,390 outputs
Outputs of similar age from BMC Medical Genomics
#3
of 11 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 1,230 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 76% 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 323,390 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.