Evidence map›Paper›PMID 23475072›Full record

ArticleNature biotechnology2013

Mutation identification by direct comparison of whole-genome sequencing data from mutant and wild-type individuals using k-mers.

Karl J V Nordström, Maria C Albani, Geo Velikkakam James, Caroline Gutjahr, Benjamin Hartwig, Franziska Turck, Uta Paszkowski, George Coupland, Korbinian Schneeberger

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Article in Nature biotechnology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.

0numbers the graph read from it
0cells of the map it votes in
88citing 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

88 citing papers in PubMed.

  1. Article
  2. Advances in mutant characterization for detecting causal mutations in crop plants.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. A survey of k-mer methods and applications in bioinformatics.Computational and structural biotechnology journal · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Fine Mapping of Stripe-Rust-Resistance GenePlants (Basel, Switzerland) · 2022
    Article
  16. Article
  17. Article
  18. Review
  19. Frontiers in plant science · 2022
    Article
  20. Article

28 more citing papers are in PubMed but not listed here.

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.

Karl J V NordströmMax Planck Institute for Plant Breeding Research, Cologne, Germany.
Maria C Albani
Geo Velikkakam James
Caroline Gutjahr
Benjamin Hartwig
Franziska Turck
Uta Paszkowski
George Coupland
Korbinian Schneeberger

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genes underlying mutant phenotypes can be isolated by combining marker discovery, genetic mapping and resequencing, but a more straightforward strategy for mapping mutations would be the direct comparison of mutant and wild-type genomes. Applying such an approach, however, is hampered by the need for reference sequences and by mutational loads that confound the unambiguous identification of causal mutations. Here we introduce NIKS (needle in the k-stack), a reference-free algorithm based on comparing k-mers in whole-genome sequencing data for precise discovery of homozygous mutations. We applied NIKS to eight mutants induced in nonreference rice cultivars and to two mutants of the nonmodel species Arabis alpina. In both species, comparing pooled F2 individuals selected for mutant phenotypes revealed small sets of mutations including the causal changes. Moreover, comparing M3 seedlings of two allelic mutants unambiguously identified the causal gene. Thus, for any species amenable to mutagenesis, NIKS enables forward genetics without requiring segregating populations, genetic maps and reference sequences.

Indexed as

AlgorithmsAllelesArabidopsisArabisBase PairingBase SequenceChromosome MappingCrosses, GeneticEthyl MethanesulfonateFlowersGenes, PlantGenome, PlantMolecular Sequence DataMutationOryzaPlant ProteinsEthyl MethanesulfonatePlant Proteins

Identifiers

PMID23475072

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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.