Evidence map›Paper›PMID 30819774›Full record

ArticleRNA (New York, N.Y.)2019

OPTIMIR, a novel algorithm for integrating available genome-wide genotype data into miRNA sequence alignment analysis.

Florian Thibord, Claire Perret, Maguelonne Roux, Pierre Suchon, Marine Germain, Jean-François Deleuze, Pierre-Emmanuel Morange, David-Alexandre Trégouët, GENMED Consortium

Abstract read
In one paragraph

Article in RNA (New York, N.Y.), 2019. 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
–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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis.European heart journal supplements : journal of the European Society of Cardiology · 2020
    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

9 authors.

Florian ThibordSorbonne Universités, Université Pierre et Marie Curie (UPMC Univ Paris 06), Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, Team Genomics and Pathophysiology of Cardiovascular Diseases, 75013 Paris, France.
Claire PerretSorbonne Universités, Université Pierre et Marie Curie (UPMC Univ Paris 06), Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, Team Genomics and Pathophysiology of Cardiovascular Diseases, 75013 Paris, France.
Maguelonne RouxSorbonne Universités, Université Pierre et Marie Curie (UPMC Univ Paris 06), Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, Team Genomics and Pathophysiology of Cardiovascular Diseases, 75013 Paris, France.
Pierre SuchonLaboratory of Haematology, La Timone Hospital, 13885 Marseille, France.
Marine GermainSorbonne Universités, Université Pierre et Marie Curie (UPMC Univ Paris 06), Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, Team Genomics and Pathophysiology of Cardiovascular Diseases, 75013 Paris, France.
Jean-François DeleuzeCentre National de Recherche en Génomique Humaine, Direction de la Recherche Fondamentale, CEA, 91057 Evry, France.
Pierre-Emmanuel MorangeLaboratory of Haematology, La Timone Hospital, 13885 Marseille, France.
David-Alexandre TrégouëtSorbonne Universités, Université Pierre et Marie Curie (UPMC Univ Paris 06), Institut National pour la Santé et la Recherche Médicale (INSERM), Unité Mixte de Recherche en Santé (UMR_S) 1166, Team Genomics and Pathophysiology of Cardiovascular Diseases, 75013 Paris, France.
GENMED Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation sequencing is an increasingly popular and efficient approach to characterize the full set of microRNAs (miRNAs) present in human biosamples. MiRNAs' detection and quantification still remain a challenge as they can undergo different posttranscriptional modifications and might harbor genetic variations (polymiRs) that may impact on the alignment step. We present a novel algorithm, OPTIMIR, that incorporates biological knowledge on miRNA editing and genome-wide genotype data available in the processed samples to improve alignment accuracy. OPTIMIR was applied to 391 human plasma samples that had been typed with genome-wide genotyping arrays. OPTIMIR was able to detect genotyping errors, suggested the existence of novel miRNAs and highlighted the allelic imbalance expression of polymiRs in heterozygous carriers. OPTIMIR is written in python, and freely available on the GENMED website (http://www.genmed.fr/index.php/fr/) and on Github (github.com/FlorianThibord/OptimiR).

Indexed as

AlgorithmsGenome, HumanBase SequenceComputational BiologyDatabases, GeneticGenome-Wide Association StudyGenotypeHeterozygoteHumansMicroRNAsOligonucleotide Array Sequence AnalysisSequence AlignmentSoftwareVenous ThrombosisMicroRNAsalignmentgenetic variationsisomiRsmicroRNAnext-generation sequencing

Identifiers

PMID30819774
PMCPMC6521604

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.