Evidence map›Paper›PMID 33329699›Full record

ArticleFrontiers in genetics2020

A Comparison of Low Read Depth QuantSeq 3' Sequencing to Total RNA-Seq in FUS Mutant Mice.

Seth Jarvis, Nicol Birsa, Maria Secrier, Pietro Fratta, Vincent Plagnol

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 26% 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

5 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

5 authors at 2 institutions in 1 country.

Seth JarvisUCL Queen Square Institute of Neurology, London, United Kingdom.
Nicol BirsaUCL Queen Square Institute of Neurology, London, United Kingdom.
Maria SecrierUCL Genetics Institute, London, United Kingdom.
Pietro FrattaUCL Queen Square Institute of Neurology, London, United Kingdom.
Vincent PlagnolUCL Genetics Institute, London, United Kingdom.
National Hospital for Neurology and Neurosurgery · GBUniversity College London · GB

Funding

Medical Research Council MR/M008606/1Medical Research Council MR/S006508/1Motor Neurone Disease Association FRATTA/JAN15/946-795Motor Neurone Disease Association PLAGNOL/OCT15/885-792
6 · The paper itself

Abstract

Transcriptomics is a developing field with new methods of analysis being produced which may hold advantages in price, accuracy, or information output. QuantSeq is a form of 3' sequencing produced by Lexogen which aims to obtain similar gene-expression information to RNA-seq with significantly fewer reads, and therefore at a lower cost. QuantSeq is also able to provide information on differential polyadenylation. We applied both QuantSeq at low read depth and total RNA-seq to the same two sets of mouse spinal cord RNAs, each comprised by four controls and four mutants related to the neurodegenerative disease amyotrophic lateral sclerosis. We found substantial differences in which genes were found to be significantly differentially expressed by the two methods. Some of this difference likely due to the difference in number of reads between our QuantSeq and RNA-seq data. Other sources of difference can be explained by the differences in the way the two methods handle genes with different primary transcript lengths and how likely each method is to find a gene to be differentially expressed at different levels of overall gene expression. This work highlights how different methods aiming to assess expression difference can lead to different results.

Indexed as

ALSbioinformaticsFused in sarcomaQuantSeqRNARNA-Seq

Identifiers

PMID33329699
PMCPMC7717943
OpenAlexW3101825216

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.