Evidence map›Paper›PMID 33099301›Full record

ArticleBMC bioinformatics2020

Calculating the most likely intron splicing orders in S. pombe, fruit fly, Arabidopsis thaliana, and humans.

Meng Li

Abstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

3 citing papers in PubMed.

  1. Article
  2. Co-transcriptional gene regulation in eukaryotes and prokaryotes.Nature reviews. Molecular cell biology · 2024
    Review
  3. 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

1 author.

Meng LiCAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China. limeng49631@aliyun.com.ORCID http://orcid.org/0000-0001-5549-4034

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIntrons have been shown to be spliced in a defined order, and this order influences both alternative splicing regulation and splicing fidelity, but previous studies have only considered neighbouring introns. The detailed intron splicing order remains unknown.

resultsIn this work, a method was developed that can calculate the intron splicing orders of all introns in each transcript. A simulation study showed that this method can accurately calculate intron splicing orders. I further applied this method to real S. pombe, fruit fly, Arabidopsis thaliana, and human sequencing datasets and found that intron splicing orders change from gene to gene and that humans contain more not in-order spliced transcripts than S. pombe, fruit fly and Arabidopsis thaliana. In addition, I reconfirmed that the first introns in humans are spliced slower than those in S. pombe, fruit fly, and Arabidopsis thaliana genome-widely. Both the calculated most likely orders and the method developed here are available on the web.

conclusionsA novel computational method was developed to calculate the intron splicing orders and applied the method to real sequencing datasets. I obtained intron splicing orders for hundreds or thousands of genes in four organisms. I found humans contain more number of not in-order spliced transcripts.

Indexed as

Alternative SplicingAnimalsArabidopsisBase SequenceComputational BiologyDrosophila melanogasterHumansIntronsRNA SplicingSchizosaccharomycesBayesian networkIntron splicing orderMost likely orderSplicing

Identifiers

PMID33099301
PMCPMC7585206

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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.