Evidence map›Paper›PMID 30577744›Full record

ArticleBMC bioinformatics2018

Detection and visualization of complex structural variants from long reads.

Zachary Stephens, Chen Wang, Ravishankar K Iyer, Jean-Pierre Kocher

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
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

15 citing papers in PubMed, 29 citations in OpenAlex.

  1. Article
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  5. Population-scale genotyping of structural variation in the era of long-read sequencing.Computational and structural biotechnology journal · 2022
    Review
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  12. Review
  13. Structural variation in the sequencing era.Nature reviews. Genetics · 2020
    Review
  14. 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

4 authors at 2 institutions in 1 country.

Zachary StephensCoordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Chen WangMayo Clinic, Rochester, MN, USA.
Ravishankar K IyerCoordinated Science Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Jean-Pierre KocherMayo Clinic, Rochester, MN, USA. Kocher.JeanPierre@mayo.edu.
Mayo Clinic in Arizona · USUniversity of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith applications in cancer, drug metabolism, and disease etiology, understanding structural variation in the human genome is critical in advancing the thrusts of individualized medicine. However, structural variants (SVs) remain challenging to detect with high sensitivity using short read sequencing technologies. This problem is exacerbated when considering complex SVs comprised of multiple overlapping or nested rearrangements. Longer reads, such as those from Pacific Biosciences platforms, often span multiple breakpoints of such events, and thus provide a way to unravel small-scale complexities in SVs with higher confidence.

resultsWe present CORGi (COmplex Rearrangement detection with Graph-search), a method for the detection and visualization of complex local genomic rearrangements. This method leverages the ability of long reads to span multiple breakpoints to untangle SVs that appear very complicated with respect to a reference genome. We validated our approach against both simulated long reads, and real data from two long read sequencing technologies. We demonstrate the ability of our method to identify breakpoints inserted in synthetic data with high accuracy, and the ability to detect and plot SVs from NA12878 germline, achieving 88.4% concordance between the two sets of sequence data. The patterns of complexity we find in many NA12878 SVs match known mechanisms associated with DNA replication and structural variant formation, and highlight the ability of our method to automatically label complex SVs with an intuitive combination of adjacent or overlapping reference transformations.

conclusionsCORGi is a method for interrogating genomic regions suspected to contain local rearrangements using long reads. Using pairwise alignments and graph search CORGi produces labels and visualizations for local SVs of arbitrary complexity.

Indexed as

Genomic Structural VariationComputer SimulationGene DuplicationGenome, HumanHumansSequence AlignmentSequence Analysis, DNASoftwareComplex rearrangementLong readStructural variation

Identifiers

PMID30577744
PMCPMC6302372
OpenAlexW2905916374

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.