SV-plaudit: A cloud-based framework for manually curating thousands of structural variants

Belyeu, Jonathan R; Nicholas, Thomas J.; Pedersen, Brent S.; Sasani, Thomas A; Havrilla, James M; Kravitz, Stephanie N.; Conway, Megan E; Lohman, Brian K.; Quinlan, Aaron R.; Layer, Ryan M.

doi:10.1093/gigascience/giy064

Cited by 44 publications

(51 citation statements)

References 28 publications

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“…For genome assembly comparisons, Illumina and Nanopore reads were aligned to Berenice genome assembled with both reads using minimap2 v2.10-r784 (Li 2018) with default parameters. Per-base genome coverage was calculated using bedtools v2.26.0 (Quinlan and Hall 2010) and samplot (Belyeu et al 2018) was used for rendering the sequencing coverage in specific genomic regions. Completeness of genome coding regions was assessed using BUSCO v3.0.2 (Simão et al 2015) with the eukaryotic and protist linages databases.…”

Section: Discussionmentioning

confidence: 99%

Nanopore sequencing significantly improves genome assembly of the eukaryotic protozoan parasite Trypanosoma cruzi

Díaz-Viraqué

Pita

Greif

et al. 2018

Preprint

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Chagas disease was described by Carlos Chagas, who first identified the parasite Trypanosoma cruzi from a two-year-old girl called Berenice. Many T. cruzi sequencing projects based on short reads have demonstrated that genome assembly and downstream comparative analyses are extremely challenging in this species, given that half of its genome is composed of repetitive sequences. Here, we report de novo assemblies, annotation and comparative analyses of the Berenice strain using a combination of Illumina short reads and MinION long reads. Our work demonstrates that Nanopore sequencing improves T. cruzi assembly contiguity and increases the assembly size in ~16Mb. Specifically, we found that assembly improvement also refines the completeness of coding regions for both single copy genes and repetitive transposable elements. Beyond its historical and epidemiological importance, Berenice constitutes a fundamental resource since it now represents the best-quality assembly available for TcII, a highly prevalent lineage causing human infections in South America. The availability of Berenice genome expands the known genetic diversity of T. cruzi and facilitates more comprehensive evolutionary inferences. Our work represents the first report of Nanopore technology used to resolve complex protozoan genomes, supporting its subsequent application for improving trypanosomatid and other highly repetitive genomes. 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

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Section: Discussionmentioning

confidence: 99%

Nanopore sequencing significantly improves genome assembly of the eukaryotic protozoan parasite Trypanosoma cruzi

Díaz-Viraqué

Pita

Greif

et al. 2018

Preprint

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“…For deletion, duplication, and inversion calls that were smaller than 1Mbp, we used a tool called samplot [19] to generate visualizations of the structural variant call for the proband and any available relatives in a case. These visualizations allowed analysts to see coverage changes and discordant read pairs for the proband and relatives at the site of the structural variant call of interest.…”

Section: Variant Analysismentioning

confidence: 99%

Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

et al. 2019

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Purpose: Clinical whole genome sequencing is becoming more common for determining the molecular diagnosis of rare disease. However, standard clinical practice often focuses on small variants such as single nucleotide variants and small insertions/deletions. This leaves a wide range of larger "structural variants" that are not commonly analyzed in patients. Methods: We developed a pipeline for processing structural variants for patients who received whole genome sequencing through the Undiagnosed Diseases Network (UDN). This pipeline called structural variants, stored them in an internal database, and filtered the variants based on internal frequencies and external annotations. The remaining variants were manually inspected and then interesting findings were reported as research variants to clinical sites in the UDN. Results: Of 477 analyzed UDN cases, 286 cases (≈ 60%) received at least one structural variant as a research finding. The variants in 16 cases (≈ 4%) are considered "Certain" or "Highly likely" molecularly diagnosed and another 4 cases are currently in review. Of those 20 cases, at least 13 were identified originally through our pipeline with one finding leading to identification of a new disease. As part of this paper, we have also released the collection of variant calls identified in our cohort along with heterozygous and homozygous call counts. This data is available at https://github.com/HudsonAlpha/UDN_SV_export. Conclusion: Structural variants are key genetic features that should be analyzed during routine clinical genomic analysis. For our UDN patients, structural variants helped solve ≈ 4% of the total number of cases (≈ 13% of all genome sequencing solves), a success rate we expect to improve with better tools and greater understanding of the human genome.

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“…Identifying a reported deletions as a false positive becomes easy and fast with these visualization tools (Belyeu et al, 2018) 2. Related Work SV-Plaudit As described above, Samplot makes it easy to be able to verify whether or not a putative SV is a True positive.…”

Section: Introductionmentioning

confidence: 99%

“…Related Work SV-Plaudit As described above, Samplot makes it easy to be able to verify whether or not a putative SV is a True positive. SV-Plaudit (Belyeu et al, 2018) is a framework built on top of Samplot and Amazon Web Services to enable manual curation of SVs using a simple web interface. SVplaudit can output a score for each reported SV based on how many annotators labelled a region as a true positive or Duphold Many SV callers make use of discordant and split reads but do not incorporate depth of coverage.…”

Section: Introductionmentioning

confidence: 99%

Learning What a Good Structural Variant Looks Like

Chowdhury

Layer²

2020

Preprint

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Structural variations (SVs) are an important class of genetic mutations, yet SV detectors still suffer from high false-positive rates. In many cases, humans can quickly determine whether a putative SV is real by merely looking at a visualization of the SV's coverage profile. To that end, we developed Samplot-ML, a convolutional neural network (CNN) trained to genotype genomic deletions using Samplot visualizations that incorporate various forms of evidence such as genome coverage, discordant pairs, and split reads. Using Samplot-ML, we were able to reduce false positives by 47% while keeping 97% of true positives on average across several test samples.

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SV-plaudit: A cloud-based framework for manually curating thousands of structural variants

Cited by 44 publications

References 28 publications

Nanopore sequencing significantly improves genome assembly of the eukaryotic protozoan parasite Trypanosoma cruzi

Nanopore sequencing significantly improves genome assembly of the eukaryotic protozoan parasite Trypanosoma cruzi

Identification of Pathogenic Structural Variants in Rare Disease Patients through Genome Sequencing

Learning What a Good Structural Variant Looks Like

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