Evidence map›Paper›PMID 25404127›Full record

ArticleNucleic acids research2015

Reference-free detection of isolated SNPs.

Raluca Uricaru, Guillaume Rizk, Vincent Lacroix, Elsa Quillery, Olivier Plantard, Rayan Chikhi, Claire Lemaitre, Pierre Peterlongo

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Article
  2. Evolution letters · 2025
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  6. Review
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  15. Article
  16. Article
  17. Article
  18. Metagenome SNP calling via read-colored de Bruijn graphs.Bioinformatics (Oxford, England) · 2021
    Article
  19. Article
  20. 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

8 authors.

Raluca UricaruUniversity of Bordeaux, CNRS/LaBRI, F-33405 Talence, France University of Bordeaux, CBiB, F-33000 Bordeaux, France INRA, UMR1349 IGEPP, Le Rheu, France ruricaru@labri.fr.
Guillaume RizkGenScale, INRIA Rennes Bretagne-Atlantique, IRISA, Rennes, France.
Vincent LacroixBAMBOO, INRIA Grenoble Rhone-Alpes, Lyon, France Laboratoire de Biométrie et Biologie Évolutive, Université Lyon 1 UMR CNRS 5558, Lyon, France.
Elsa QuilleryINRA, UMR1300 Biology, Epidemiology and Risk Analysis in Animal Health, Nantes, France LUNAM University, Oniris, Nantes Atlantic College of Veterinary Medicine and Food Sciences and Engineering, UMR BioEpAR, Nantes, France.
Olivier PlantardINRA, UMR1300 Biology, Epidemiology and Risk Analysis in Animal Health, Nantes, France LUNAM University, Oniris, Nantes Atlantic College of Veterinary Medicine and Food Sciences and Engineering, UMR BioEpAR, Nantes, France.
Rayan ChikhiDepartment of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.
Claire LemaitreGenScale, INRIA Rennes Bretagne-Atlantique, IRISA, Rennes, France claire.lemaitre@inria.fr.
Pierre PeterlongoGenScale, INRIA Rennes Bretagne-Atlantique, IRISA, Rennes, France pierre.peterlongo@inria.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting single nucleotide polymorphisms (SNPs) between genomes is becoming a routine task with next-generation sequencing. Generally, SNP detection methods use a reference genome. As non-model organisms are increasingly investigated, the need for reference-free methods has been amplified. Most of the existing reference-free methods have fundamental limitations: they can only call SNPs between exactly two datasets, and/or they require a prohibitive amount of computational resources. The method we propose, discoSnp, detects both heterozygous and homozygous isolated SNPs from any number of read datasets, without a reference genome, and with very low memory and time footprints (billions of reads can be analyzed with a standard desktop computer). To facilitate downstream genotyping analyses, discoSnp ranks predictions and outputs quality and coverage per allele. Compared to finding isolated SNPs using a state-of-the-art assembly and mapping approach, discoSnp requires significantly less computational resources, shows similar precision/recall values, and highly ranked predictions are less likely to be false positives. An experimental validation was conducted on an arthropod species (the tick Ixodes ricinus) on which de novo sequencing was performed. Among the predicted SNPs that were tested, 96% were successfully genotyped and truly exhibited polymorphism.

Indexed as

Polymorphism, Single NucleotideAlgorithmsAnimalsChromosomes, Human, Pair 1Escherichia coliGenomicsGenotyping TechniquesHumansIxodesMiceMice, Inbred C57BLSaccharomyces cerevisiae

Identifiers

PMID25404127
PMCPMC4333369

What OpenQuestion holds

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LicenceCC BY
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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.