Evidence map›Paper›PMID 26334759›Full record

ArticleBMC genomics2015

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

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

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

60 citing papers in PubMed, 117 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Shanrong ZhaoClinical Genetics and Bioinformatics, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. Shanrong.Zhao@pfizer.com.
Ying ZhangPrecision Medicine - Bioanalytical, PTx Clinical R&D, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. Ying.Zhang1@pfizer.com.
William GordonPrecision Medicine - Bioanalytical, PTx Clinical R&D, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. William.Gordon@pfizer.com.
Jie QuanComputational Sciences Centers of Excellence, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. Jie.Quan@pfizer.com.
Hualin XiComputational Sciences Centers of Excellence, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. hualin.xi@pfizer.com.
Sarah DuPrecision Medicine - Bioanalytical, PTx Clinical R&D, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. sarah.du@pfizer.com.
David von SchackPrecision Medicine - Bioanalytical, PTx Clinical R&D, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. david.vonschack@pfizer.com.
Baohong ZhangClinical Genetics and Bioinformatics, Pfizer Worldwide Research & Development, Cambridge, MA, 02139, USA. Baohong.Zhang@pfizer.com.
Pfizer (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWhile 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.

resultsOur 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.

conclusionsStranded 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.

Indexed as

Gene Expression ProfilingGenesCell Adhesion MoleculesHumansInterleukin-24InterleukinsMaleReproducibility of ResultsSequence Analysis, RNACell Adhesion MoleculesICAM4 protein, humanInterleukin-24Interleukins

Identifiers

PMID26334759
PMCPMC4559181
OpenAlexW1763607657

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.