Evidence map›Paper›PMID 23180801›Full record

ArticleNucleic acids research2013

Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching.

Dave T P Tang, Charles Plessy, Md Salimullah, Ana Maria Suzuki, Raffaella Calligaris, Stefano Gustincich, Piero Carninci

Abstract read
In one paragraph

Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–field-weighted citation impact
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

45 citing papers in PubMed.

  1. Article
  2. Review
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  5. Efficient profiling of total RNA in single cells with STORM-seq.bioRxiv : the preprint server for biology · 2025
    Article
  6. Decoding meLife · 2025
    Article
  7. Article
  8. Transcriptome and Temporal Transcriptome Analyses in Single Cells.International journal of molecular sciences · 2024
    Review
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
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  17. Review
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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

7 authors.

Dave T P TangOmics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Yokohama, Kanagawa 230-0045, Japan.
Charles Plessy
Md Salimullah
Ana Maria Suzuki
Raffaella Calligaris
Stefano Gustincich
Piero Carninci

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Template switching (TS) has been an inherent mechanism of reverse transcriptase, which has been exploited in several transcriptome analysis methods, such as CAGE, RNA-Seq and short RNA sequencing. TS is an attractive option, given the simplicity of the protocol, which does not require an adaptor mediated step and thus minimizes sample loss. As such, it has been used in several studies that deal with limited amounts of RNA, such as in single cell studies. Additionally, TS has also been used to introduce DNA barcodes or indexes into different samples, cells or molecules. This labeling allows one to pool several samples into one sequencing flow cell, increasing the data throughput of sequencing and takes advantage of the increasing throughput of current sequences. Here, we report TS artifacts that form owing to a process called strand invasion. Due to the way in which barcodes/indexes are introduced by TS, strand invasion becomes more problematic by introducing unsystematic biases. We describe a strategy that eliminates these artifacts in silico and propose an experimental solution that suppresses biases from TS.

Indexed as

ArtifactsAnimalsGene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMiceRatsRNASensitivity and SpecificitySequence Analysis, RNATemplates, GeneticRNA

Identifiers

PMID23180801
PMCPMC3562004

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.