Evidence map›Paper›PMID 32028886›Full record

ArticleBMC genomics2020

iREAD: a tool for intron retention detection from RNA-seq data.

Hong-Dong Li, Cory C Funk, Nathan D Price

Abstract read
In one paragraph

Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
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  4. Review
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  8. SON-dependent nuclear speckle rehabilitation alleviates proteinopathies.bioRxiv : the preprint server for biology · 2025
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  14. Evidence for ~12-h ultradian gene programs in humans.Npj biological timing and sleep · 2024
    Article
  15. Article
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  19. mCell death & disease · 2023
    Article
  20. Unleashing the potential of catalytic RNAs to combat mis-spliced transcripts.Frontiers in bioengineering and biotechnology · 2023
    Review
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

3 authors.

Hong-Dong LiCenter for Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan Province, 410083, People's Republic of China.
Cory C FunkInstitute for Systems Biology, Seattle, WA, 98109, USA.
Nathan D PriceInstitute for Systems Biology, Seattle, WA, 98109, USA. Nathan.Price@systemsbiology.org.ORCID http://orcid.org/0000-0002-4157-0267

Funding

SUPPLEMENT TO ALZHEIMERS DISEASE PATIENT REGISTRYU01AG006786 · NIA · MAYO CLINIC ROCHESTER · PI GRAFF-RADFORD, JONATHAN, JACK, CLIFFORD R. · 1986 to 2023
$49.6M
NIA NIH HHS U01 AG006786
6 · The paper itself

Abstract

backgroundIntron retention (IR) has been traditionally overlooked as 'noise' and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection.

resultsHere we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread.

conclusionBeing complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome.

Indexed as

IntronsRNA-SeqSoftwareAlgorithmsAnimalsHumansMiceGene expressionIntron retentioniREADRNA-seq

Identifiers

PMID32028886
PMCPMC7006120

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.