Evidence map›Paper›PMID 34698333›Full record

ArticleBriefings in bioinformatics2022

Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2.

Enrico Gaffo, Alessia Buratin, Anna Dal Molin, Stefania Bortoluzzi

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

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  10. Regulatory Landscapes of Non-Coding RNAs During Drought Stress in Plants.International journal of molecular sciences · 2025
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  18. Extending scope and power of circular RNA research with circtools 2.0.bioRxiv : the preprint server for biology · 2025
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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.

Enrico GaffoDepartment of Molecular Medicine, University of Padova, Padova, Italy.ORCID 0000-0001-6338-7677
Alessia BuratinDepartment of Molecular Medicine, University of Padova, Padova, Italy; Department of Biology, University of Padova, Padova, Italy.
Anna Dal MolinDepartment of Molecular Medicine, University of Padova, Padova, Italy.
Stefania BortoluzziDepartment of Molecular Medicine, University of Padova, Padova, Italy; Interdepartmental Center For Innovative Biotechnologies, University of Padova, Padova, Italy.ORCID 0000-0001-8240-3070

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2), a new automated computational pipeline for circRNA discovery and quantification, which consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptome characterization.

Indexed as

RNA, CircularTranscriptomeExome SequencingRNARNA-SeqRNA SplicingRNARNA, CircularbioinformaticscircRNAscomputational pipelineRNA-seq

Identifiers

PMID34698333
PMCPMC8769706

What OpenQuestion holds

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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.