Evidence map›Paper›PMID 30598110›Full record

ArticleBMC genomics2018

Mining statistically-solid k-mers for accurate NGS error correction.

Liang Zhao, Jin Xie, Lin Bai, Wen Chen, Mingju Wang, Zhonglei Zhang, Yiqi Wang, Zhe Zhao, Jinyan Li

Abstract read
In one paragraph

Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Fine mapping and candidate gene analysis of qPH19.1, a locus for soybean (Glycine max L.) plant height.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  6. Gene Mining and Flavour Metabolism Analyses ofFrontiers in microbiology · 2022
    Article
  7. Article
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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

9 authors.

Liang ZhaoPrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China. s080011@e.ntu.edu.sg.
Jin XiePrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Lin BaiSchool of Computing and Electronic Information, Guangxi University, Nanning, China.
Wen ChenPrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Mingju WangPrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Zhonglei ZhangPrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Yiqi WangPrecision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Zhe ZhaoSchool of Computing and Electronic Information, Guangxi University, Nanning, China.
Jinyan LiAdvanced Analytics Institute, Faculty of Engineering & IT, University of Technology Sydney, NSW 2007, Australia. jinyan.li@uts.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f

resultsWe propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f

conclusionThe z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.

Indexed as

Data MiningHigh-Throughput Nucleotide SequencingResearch DesignAlgorithmsError correctionNext-generation sequencingz-score

Identifiers

PMID30598110
PMCPMC6311904

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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.