↓ Skip to main content

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

Overview of attention for article published in Nature Biotechnology, March 2013
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Citations

dimensions_citation
141 Dimensions

Readers on

mendeley
480 Mendeley
citeulike
5 CiteULike
Title
Mutation identification by direct comparison of whole-genome sequencing data from mutant and wild-type individuals using k-mers
Published in
Nature Biotechnology, March 2013
DOI 10.1038/nbt.2515
Pubmed ID
Authors

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

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 45 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 480 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 3%
Netherlands 7 1%
France 5 1%
Germany 4 <1%
China 4 <1%
Belgium 3 <1%
United Kingdom 3 <1%
India 2 <1%
Sweden 1 <1%
Other 10 2%
Unknown 428 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 141 29%
Student > Ph. D. Student 131 27%
Student > Master 39 8%
Professor > Associate Professor 24 5%
Student > Bachelor 22 5%
Other 79 16%
Unknown 44 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 296 62%
Biochemistry, Genetics and Molecular Biology 68 14%
Computer Science 31 6%
Medicine and Dentistry 7 1%
Immunology and Microbiology 6 1%
Other 20 4%
Unknown 52 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 40. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 June 2021.
All research outputs
#1,020,155
of 25,394,764 outputs
Outputs from Nature Biotechnology
#1,871
of 8,558 outputs
Outputs of similar age
#7,280
of 208,704 outputs
Outputs of similar age from Nature Biotechnology
#20
of 108 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,558 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.2. This one has done well, scoring higher than 78% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 208,704 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.