Evidence map›Paper›PMID 36202992›Full record

ArticleCommunications biology2022

SoloTE for improved analysis of transposable elements in single-cell RNA-Seq data using locus-specific expression.

Rocío Rodríguez-Quiroz, Braulio Valdebenito-Maturana

Open access · goldAbstract read
In one paragraph

Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
4.9field-weighted citation impact, top 3% 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

42 citing papers in PubMed, 63 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. Article
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

2 authors at 1 institution in 1 country.

Rocío Rodríguez-QuirozDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Braulio Valdebenito-MaturanaDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA. braulio.valdebenitomaturana@mssm.edu.
Icahn School of Medicine at Mount Sinai · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transposable Elements (TEs) contribute to the repetitive fraction in almost every eukaryotic genome known to date, and their transcriptional activation can influence the expression of neighboring genes in healthy and disease states. Single cell RNA-Seq (scRNA-Seq) is a technical advance that allows the study of gene expression on a cell-by-cell basis. Although a current computational approach is available for the single cell analysis of TE expression, it omits their genomic location. Here we show SoloTE, a pipeline that outperforms the previous approach in terms of computational resources and by allowing the inclusion of locus-specific TE activity in scRNA-Seq expression matrixes. We then apply SoloTE to several datasets to reveal the repertoire of TEs that become transcriptionally active in different cell groups, and based on their genomic location, we predict their potential impact on gene expression. As our tool takes as input the resulting files from standard scRNA-Seq processing pipelines, we expect it to be widely adopted in single cell studies to help researchers discover patterns of cellular diversity associated with TE expression.

Indexed as

DNA Transposable ElementsGenomicsEukaryotaRNA-SeqSingle-Cell AnalysisDNA Transposable Elements

Identifiers

PMID36202992
PMCPMC9537157
OpenAlexW4302424857

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.