Evidence map›Paper›PMID 29556074›Full record

ArticleScientific reports2018

Evaluation of two main RNA-seq approaches for gene quantification in clinical RNA sequencing: polyA+ selection versus rRNA depletion.

Shanrong Zhao, Ying Zhang, Ramya Gamini, Baohong Zhang, David von Schack

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 125 papers.

0numbers the graph read from it
0cells of the map it votes in
125citing papers in PubMed
7.2field-weighted citation impact, top 1% 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

125 citing papers in PubMed, 227 citations in OpenAlex.

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  20. Low-volumeMicrobial genomics · 2025
    Article

65 more citing papers are in PubMed but not listed here.

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

5 authors at 1 institution in 1 country.

Shanrong ZhaoPrecision Medicine, Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, 02139, MA, USA. Shanrong.Zhao@pfizer.com.
Ying ZhangInflammation & Immunology Research Unit, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.
Ramya GaminiPrecision Medicine, Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, 02139, MA, USA.
Baohong ZhangPrecision Medicine, Early Clinical Development, Pfizer Worldwide Research and Development, Cambridge, 02139, MA, USA.ORCID 0000-0003-4735-4212
David von SchackInflammation & Immunology Research Unit, Pfizer Worldwide Research and Development, Cambridge, MA, 02139, USA.
Pfizer (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To allow efficient transcript/gene detection, highly abundant ribosomal RNAs (rRNA) are generally removed from total RNA either by positive polyA+ selection or by rRNA depletion (negative selection) before sequencing. Comparisons between the two methods have been carried out by various groups, but the assessments have relied largely on non-clinical samples. In this study, we evaluated these two RNA sequencing approaches using human blood and colon tissue samples. Our analyses showed that rRNA depletion captured more unique transcriptome features, whereas polyA+ selection outperformed rRNA depletion with higher exonic coverage and better accuracy of gene quantification. For blood- and colon-derived RNAs, we found that 220% and 50% more reads, respectively, would have to be sequenced to achieve the same level of exonic coverage in the rRNA depletion method compared with the polyA+ selection method. Therefore, in most cases we strongly recommend polyA+ selection over rRNA depletion for gene quantification in clinical RNA sequencing. Our evaluation revealed that a small number of lncRNAs and small RNAs made up a large fraction of the reads in the rRNA depletion RNA sequencing data. Thus, we recommend that these RNAs are specifically depleted to improve the sequencing depth of the remaining RNAs.

Indexed as

HumansPoly ARNA, RibosomalSequence Analysis, RNAPoly ARNA, Ribosomal

Identifiers

PMID29556074
PMCPMC5859127
OpenAlexW2790416670

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.