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Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap

Overview of attention for article published in BMC Genomics, September 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 (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

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1 news outlet
blogs
2 blogs
twitter
8 X users

Citations

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84 Dimensions

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556 Mendeley
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1 CiteULike
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Title
Comparison of stranded and non-stranded RNA-seq transcriptome profiling and investigation of gene overlap
Published in
BMC Genomics, September 2015
DOI 10.1186/s12864-015-1876-7
Pubmed ID
Authors

Shanrong Zhao, Ying Zhang, William Gordon, Jie Quan, Hualin Xi, Sarah Du, David von Schack, Baohong Zhang

Abstract

While RNA-sequencing (RNA-seq) is becoming a powerful technology in transcriptome profiling, one significant shortcoming of the first-generation RNA-seq protocol is that it does not retain the strand specificity of origin for each transcript. Without strand information it is difficult and sometimes impossible to accurately quantify gene expression levels for genes with overlapping genomic loci that are transcribed from opposite strands. It has recently become possible to retain the strand information by modifying the RNA-seq protocol, known as strand-specific or stranded RNA-seq. Here, we evaluated the advantages of stranded RNA-seq in transcriptome profiling of whole blood RNA samples compared with non-stranded RNA-seq, and investigated the influence of gene overlaps on gene expression profiling results based on practical RNA-seq datasets and also from a theoretical perspective. Our results demonstrated a substantial impact of stranded RNA-seq on transcriptome profiling and gene expression measurements. As many as 1751 genes in Gencode Release 19 were identified to be differentially expressed when comparing stranded and non-stranded RNA-seq whole blood samples. Antisense and pseudogenes were significantly enriched in differential expression analyses. Because stranded RNA-seq retains strand information of a read, we can resolve read ambiguity in overlapping genes transcribed from opposite strands, which provides a more accurate quantification of gene expression levels compared with traditional non-stranded RNA-seq. In the human genome, it is not uncommon to find genomic loci where both strands encode distinct genes. Among the over 57,800 annotated genes in Gencode release 19, there are an estimated 19 % (about 11,000) of overlapping genes transcribed from the opposite strands. Based on our whole blood mRNA-seq datasets, the fraction of overlapping nucleotide bases on the same and opposite strands were estimated at 2.94 % and 3.1 %, respectively. The corresponding theoretical estimations are 3 % and 3.6 %, well in agreement with our own findings. Stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq, and is therefore the recommended RNA-seq approach for future mRNA-seq studies.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 556 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 <1%
United States 3 <1%
Brazil 2 <1%
Sweden 2 <1%
Spain 2 <1%
South Africa 1 <1%
Czechia 1 <1%
United Kingdom 1 <1%
Norway 1 <1%
Other 4 <1%
Unknown 536 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 135 24%
Researcher 104 19%
Student > Master 78 14%
Student > Bachelor 49 9%
Student > Postgraduate 23 4%
Other 65 12%
Unknown 102 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 186 33%
Agricultural and Biological Sciences 156 28%
Medicine and Dentistry 18 3%
Immunology and Microbiology 14 3%
Computer Science 13 2%
Other 50 9%
Unknown 119 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 11 December 2018.
All research outputs
#1,515,388
of 24,835,287 outputs
Outputs from BMC Genomics
#284
of 11,086 outputs
Outputs of similar age
#20,184
of 272,604 outputs
Outputs of similar age from BMC Genomics
#6
of 283 outputs
Altmetric has tracked 24,835,287 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,086 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 97% 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 272,604 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 283 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.