Evidence map›Paper›PMID 30967123›Full record

ArticleBMC genomics2019

Improving the sensitivity of long read overlap detection using grouped short k-mer matches.

Nan Du, Jiao Chen, Yanni Sun

Abstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. MELO-ED: learning locality-sensitive multi-embeddings for edit distance.bioRxiv : the preprint server for biology · 2025
    Article
  2. Seeding with minimized subsequence.Bioinformatics (Oxford, England) · 2023
    Article
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Nan DuDepartment of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA.
Jiao ChenDepartment of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA.
Yanni SunElectronic Engineering Department, City University of Hong Kong, Hong Kong SAR, China. yannisun@cityu.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSingle-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than second-generation sequencing technologies such as Illumina. The increased read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and characterize the intra-species variations. It also holds the promise to decipher the community structure in complex microbial communities because long reads help metagenomic assembly. One key step in genome assembly using long reads is to quickly identify reads forming overlaps. Because PacBio data has higher sequencing error rate and lower coverage than popular short read sequencing technologies (such as Illumina), efficient detection of true overlaps requires specially designed algorithms. In particular, there is still a need to improve the sensitivity of detecting small overlaps or overlaps with high error rates in both reads. Addressing this need will enable better assembly for metagenomic data produced by third-generation sequencing technologies.

resultsIn this work, we designed and implemented an overlap detection program named GroupK, for third-generation sequencing reads based on grouped k-mer hits. While using k-mer hits for detecting reads' overlaps has been adopted by several existing programs, our method uses a group of short k-mer hits satisfying statistically derived distance constraints to increase the sensitivity of small overlap detection. Grouped k-mer hit was originally designed for homology search. We are the first to apply group hit for long read overlap detection. The experimental results of applying our pipeline to both simulated and real third-generation sequencing data showed that GroupK enables more sensitive overlap detection, especially for datasets of low sequencing coverage.

conclusionsGroupK is best used for detecting small overlaps for third-generation sequencing data. It provides a useful supplementary tool to existing ones for more sensitive and accurate overlap detection. The source code is freely available at https://github.com/Strideradu/GroupK .

Indexed as

AlgorithmsMetagenomicsSoftwareDatabases, GeneticEscherichia coliHigh-Throughput Nucleotide SequencingMolecular Sequence AnnotationSequence Analysis, DNAGroup hit criteriaMetagenomicsOverlap detectionThird-generation sequencing

Identifiers

PMID30967123
PMCPMC6456931

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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.