Evidence map›Paper›PMID 38605047›Full record

ArticleNature communications2024

KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost.

Qian Zhou, Fahu Ji, Dongxiao Lin, Xianming Liu, Zexuan Zhu, Jue Ruan

Abstract read
In one paragraph

Article in Nature communications, 2024. 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. Article
  2. Article
  3. Article
  4. Article
  5. Long and Accurate: How HiFi Sequencing is Transforming Genomics.Genomics, proteomics & bioinformatics · 2025
    Review
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

6 authors.

Qian Zhou *PengCheng Laboratory, Shenzhen, China.ORCID http://orcid.org/0000-0002-5269-8178
Fahu Ji *School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.ORCID http://orcid.org/0000-0001-8318-835X
Dongxiao Lin *College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Xianming LiuPengCheng Laboratory, Shenzhen, China.ORCID http://orcid.org/0000-0002-8857-1785
Zexuan ZhuCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. zhuzx@szu.edu.cn.ORCID http://orcid.org/0000-0001-8479-6904
Jue RuanShenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. ruanjue@caas.cn.ORCID http://orcid.org/0000-0003-3713-3192

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31822029
6 · The paper itself

Abstract

Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.

Indexed as

AlgorithmsPolymorphism, Single NucleotideHaplotypesHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNASoftware

Identifiers

PMID38605047
PMCPMC11009271

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Registered trials

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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.