Evidence map›Paper›PMID 37122996›Full record

ArticleFrontiers in bioinformatics2023

Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome techniques.

Ricardo R Pavan, Fabiola Diniz, Samir El-Dahr, Giovane G Tortelote

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

4 authors at 2 institutions in 2 countries.

Ricardo R PavanInstitute for Marine and Antarctic Studies (IMAS), Nubeena Crescent, Taroona, TAS, Australia.
Fabiola DinizSection of Pediatric Nephrology, Department of Pediatrics Tulane University School of Medicine, New Orleans, LA, United States.
Samir El-DahrSection of Pediatric Nephrology, Department of Pediatrics Tulane University School of Medicine, New Orleans, LA, United States.
Giovane G TorteloteSection of Pediatric Nephrology, Department of Pediatrics Tulane University School of Medicine, New Orleans, LA, United States.
Tulane University · USAustralian Institute of Marine Science · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The scale and capability of single-cell and single-nucleus RNA-sequencing technologies are rapidly growing, enabling key discoveries and large-scale cell mapping operations. However, studies directly comparing technical differences between single-cell and single-nucleus RNA sequencing are still lacking. Here, we compared three paired single-cell and single-nucleus transcriptomes from three different organs (Heart, Lung and Kidney). Differently from previous studies that focused on cell classification, we explored disparities in the transcriptome output of whole cells relative to the nucleus. We found that the major cell clusters could be recovered by either technique from matched samples, but at different proportions. In 2/3 datasets (kidney and lung) we detected clusters exclusively present with single-nucleus RNA sequencing. In all three organ groups, we found that genomic and gene structural characteristics such as gene length and exon content significantly differed between the two techniques. Genes recovered with the single-nucleus RNA sequencing technique had longer sequence lengths and larger exon counts, whereas single-cell RNA sequencing captured short genes at higher rates. Furthermore, we found that when compared to the whole host genome (mouse for kidney and lung datasets and human for the heart dataset), single transcriptomes obtained with either technique skewed from the expected proportions in several points: a) coding sequence length, b) transcript length and c) genomic span; and d) distribution of genes based on exons counts. Interestingly, the top-100 DEG between the two techniques returned distinctive GO terms. Hence, the type of single transcriptome technique used affected the outcome of downstream analysis. In summary, our data revealed both techniques present disparities in RNA capture. Moreover, the biased RNA capture affected the calculations of basic cellular parameters, raising pivotal points about the limitations and advantages of either single transcriptome techniques.

Indexed as

biased gene capturebioinformaticdata analysisnext-generation sequencingsingle-cell RNA sequencingsingle-nucleus RNA sequencing

Identifiers

PMID37122996
PMCPMC10132733
OpenAlexW4365148125

What OpenQuestion holds

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LicenceCC BY
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.