Precise characterization of somatic complex structural variations from paired long-read sequencing data with nanomonsv

Shiraishi, Yuichi; Koya, Junji; Chiba, Kenichi; Saito, Yuki; Okada, Ai; Kataoka, Keisuke

doi:10.1101/2020.07.22.214262

Cited by 19 publications

(15 citation statements)

References 106 publications

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“…In this article, we provided a succinct yet comprehensive evaluation of long-read SV calling pipelines applied to ONT data. In particular, we focused on germline SVs, and as such, our findings are likely not reproducible in different contexts, such as somatic variant calling, for which alternative strategies exist (Shiraishi et al, 2020). We tested four general-purpose SV callers (Sniffles, SVIM, cuteSV, and pbsv) and a tool tailored specifically to inversions (npInv) across four long-read aligners (minimap2, NGMLR, lra, and pbmm2) using both real and simulated ONT data.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Bolognini¹,

Magi²

2021

Front. Genet.

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Structural variants (SVs) are genomic rearrangements that involve at least 50 nucleotides and are known to have a serious impact on human health. While prior short-read sequencing technologies have often proved inadequate for a comprehensive assessment of structural variation, more recent long reads from Oxford Nanopore Technologies have already been proven invaluable for the discovery of large SVs and hold the potential to facilitate the resolution of the full SV spectrum. With many long-read sequencing studies to follow, it is crucial to assess factors affecting current SV calling pipelines for nanopore sequencing data. In this brief research report, we evaluate and compare the performances of five long-read SV callers across four long-read aligners using both real and synthetic nanopore datasets. In particular, we focus on the effects of read alignment, sequencing coverage, and variant allele depth on the detection and genotyping of SVs of different types and size ranges and provide insights into precision and recall of SV callsets generated by integrating the various long-read aligners and SV callers. The computational pipeline we propose is publicly available at https://github.com/davidebolo1993/EViNCe and can be adjusted to further evaluate future nanopore sequencing datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Bolognini¹,

Magi²

2021

Front. Genet.

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“…To achieve the potential of long-read sequencing in cancer genomics, the development of a comprehensive suite of analytical methods tailored explicitly for tumour analysis is imperative. Existing tools are often unsuited for cancer analysis or have been tested solely on cell-line data 19,20 . Patient-derived cancer samples encompass diverse features, including tumour heterogeneity, normal cell contamination from distinct tissues, and variability in mutation burden 1,2 , mandating analytical method refinement.…”

Section: Mainmentioning

confidence: 99%

Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes

O’Neill,

Pleasance,

Fan

et al. 2024

Preprint

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The Long-read POG dataset comprises a cohort of 189 patient tumours and 41 matched normal samples sequenced using the Oxford Nanopore Technologies PromethION platform. This dataset from the Personalized Oncogenomics (POG) program and the Marathon of Hope Cancer Centres Network includes accompanying DNA and RNA short-read sequence data, analytics, and clinical information. We show the potential of long-read sequencing for resolving complex cancer-related structural variants, viral integrations, and extrachromosomal circular DNA. Long-range phasing of variants facilitates the discovery of allelically differentially methylated regions (aDMRs) and allele-specific expression, including recurrent aDMRs in the cancer genesRETandCDKN2A. Germline promoter methylation inMLH1can be directly observed in Lynch syndrome. Promoter methylation inBRCA1andRAD51Cis a likely driver behind patterns of homologous recombination deficiency where no driver mutation was found. This dataset demonstrates applications for long-read sequencing in precision medicine, and is available as a resource for developing analytical approaches using this technology.

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“…Another program called FrEIA uses variations in the sequences at the ends of cfDNA fragments to improve the sensitivity of cancer signal detection [ 252 , 253 ]. Nanomonsv is designed to detect somatic cancer-associated structural variants in paired tumor and normal samples [ 254 , 255 ]. Nanovar is a SV caller with the ability of detecting variants from low-depth, long-read sequencing (homozygous SVs can be detected using 4×, while heterozygous SVs are detected at a threshold of 8×) [ 256 ].…”

Section: Bioinformatics Pipelines For Analyzing Liquid Biopsy Ngs Datamentioning

confidence: 99%

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

Brockley¹,

Souza

Forder³

et al. 2023

Cancers

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Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

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Precise characterization of somatic complex structural variations from paired long-read sequencing data with nanomonsv

Cited by 19 publications

References 106 publications

Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Evaluation of Germline Structural Variant Calling Methods for Nanopore Sequencing Data

Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer

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