Evidence map›Paper›PMID 34224879›Full record

ArticleGenomics, proteomics & bioinformatics2022

Mako: A Graph-based Pattern Growth Approach to Detect Complex Structural Variants.

Jiadong Lin, Xiaofei Yang, Walter Kosters, Tun Xu, Yanyan Jia, Songbo Wang, Qihui Zhu, Mallory Ryan, Li Guo, Chengsheng Zhang and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. Population-scale genotyping of structural variation in the era of long-read sequencing.Computational and structural biotechnology journal · 2022
    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

15 authors.

Jiadong LinSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Genome Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; Leiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, Leiden 2311EZ, Netherland.
Xiaofei YangMOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Walter KostersLeiden Institute of Advanced Computer Science, Faculty of Science, Leiden University, Leiden 2311EZ, Netherland.
Tun XuSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Yanyan JiaSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Songbo WangSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Qihui ZhuThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Mallory RyanThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Li GuoMOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Chengsheng ZhangThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Precision Medicine Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Human Genome Structural Variation Consortium
Charles LeeThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Precision Medicine Center, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
Scott E DevineSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
Evan E EichlerDepartment of Genome Sciences, University of Washington School of Medicine, Seattle, WA 98119, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
Kai YeSchool of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China; Genome Institute, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; The School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China. Electronic address: kaiye@xjtu.edu.cn.

Funding

Shared Resource ManagementP30CA034196 · NCI · JACKSON LABORATORY · PI William Carl Skarnes · 1985 to 2026
$61.9M
Identifying and Characterizing the Full Spectrum of Haplotype-resolved Structural Variation in Human GenomesU24HG007497 · NHGRI · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI Evan Eichler, Jan Oliver Korbel · 2019 to 2026
$17.2M
Natural Genetic Variation in the Human GenomeR01HG002898 · NHGRI · UNIVERSITY OF MARYLAND BALTIMORE · PI DEVINE, SCOTT E · 2004 to 2019
$3.7M
Structural variation analysis with and without a reference genomeR35GM138212 · NIGMS · UNIVERSITY OF VIRGINIA · PI Zechen Chong · 2020 to 2026
$2.6M
Therapeutic/Mechanistic Role of Ape1 in Germ Cell TumorsR01CA094025 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI KELLEY, MARK R. · 2003 to 2007
$1.1M
NCI NIH HHS P30 CA034196NCI NIH HHS R01 CA094025NHGRI NIH HHS R01 HG002898NHGRI NIH HHS U24 HG007497NIGMS NIH HHS R35 GM138212Wellcome Trust
6 · The paper itself

Abstract

Complex structural variants (CSVs) are genomic alterations that have more than two breakpoints and are considered as the simultaneous occurrence of simple structural variants. However, detecting the compounded mutational signals of CSVs is challenging through a commonly used model-match strategy. As a result, there has been limited progress for CSV discovery compared with simple structural variants. Here, we systematically analyzed the multi-breakpoint connection feature of CSVs, and proposed Mako, utilizing a bottom-up guided model-free strategy, to detect CSVs from paired-end short-read sequencing. Specifically, we implemented a graph-based pattern growth approach, where the graph depicts potential breakpoint connections, and pattern growth enables CSV detection without pre-defined models. Comprehensive evaluations on both simulated and real datasets revealed that Mako outperformed other algorithms. Notably, validation rates of CSVs on real data based on experimental and computational validations as well as manual inspections are around 70%, where the medians of experimental and computational breakpoint shift are 13 bp and 26 bp, respectively. Moreover, the Mako CSV subgraph effectively characterized the breakpoint connections of a CSV event and uncovered a total of 15 CSV types, including two novel types of adjacent segment swap and tandem dispersed duplication. Further analysis of these CSVs also revealed the impact of sequence homology on the formation of CSVs. Mako is publicly available at https://github.com/xjtu-omics/Mako.

Indexed as

AlgorithmsGenomicsGenomeHigh-Throughput Nucleotide SequencingMutationSequence Analysis, DNAComplex structural variantFormation mechanismGraph miningNext-generation sequencingPattern growth

Identifiers

PMID34224879
PMCPMC9510932

What OpenQuestion holds

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LicenceCC BY
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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.