GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

Cameron, Daniel; Baber, Jonathan; Shale, Charles; Valle-Inclán, Jose Espejo; Besselink, Nicolle; Hoeck, Arne van; Janssen, Roel; Cuppen, Edwin; Priestley, Peter; Papenfuss, Anthony T.

doi:10.1186/s13059-021-02423-x

Cited by 104 publications

(100 citation statements)

References 39 publications

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“…As recommended, GRIDSS2( 28 ) was used for local assembly prior to running VIRUSBreakend. On one sample bam file, TCGA-CR-5243, FastViFi took 61 cpu-minutes including 37 minutes of the alignment-based filter (Section Material and Methods:pre-processing).…”

Section: Resultsmentioning

confidence: 99%

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

Javadzadeh

Rajkumar

Nguyen

et al. 2022

NAR Genomics and Bioinformatics

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DNA viruses are important infectious agents known to mediate a large number of human diseases, including cancer. Viral integration into the host genome and the formation of hybrid transcripts are also associated with increased pathogenicity. The high variability of viral genomes, however requires the use of sensitive ensemble hidden Markov models that add to the computational complexity, often requiring > 40 CPU-hours per sample. Here, we describe FastViFi, a fast 2-stage filtering method that reduces the computational burden. On simulated and cancer genomic data, FastViFi improved the running time by 2 orders of magnitude with comparable accuracy on challenging data sets. Recently published methods have focused on identification of location of viral integration into the human host genome using local assembly, but do not extend to RNA. To identify human viral hybrid transcripts, we additionally developed ensemble Hidden Markov Models for the Epstein Barr virus (EBV) to add to the models for Hepatitis B (HBV), Hepatitis C (HCV) viruses and the Human Papillomavirus (HPV), and used FastViFi to query RNA-seq data from Gastric cancer (EBV) and liver cancer (HBV/HCV). FastViFi ran in <10 minutes per sample and identified multiple hybrids that fuse viral and human genes suggesting new mechanisms for oncoviral pathogenicity. FastViFi is available at https://github.com/sara-javadzadeh/FastViFi.

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Section: Resultsmentioning

confidence: 99%

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

Javadzadeh

Rajkumar

Nguyen

et al. 2022

NAR Genomics and Bioinformatics

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“…StructuralVariantAnnotation is a library and not itself a fully fledged analysis/benchmarking tool. Capabilities such as rearrangement classification, ensemble calling, benchmarking ( Cameron et al , 2019 ) and sequencing-based variant matching logic ( Cameron et al , 2021 ) can be implemented on top of the StructuralVariantAnnotation library.…”

Section: Methodsmentioning

confidence: 99%

“…While copy number segments are easily and naturally represented using the GenomicRanges Bioconductor package ( Lawrence et al , 2013 ), representing breakpoints is less straight-forward. Although classes like InteractionSet ( Lun et al , 2016 ) and Pairs ( Pagès et al , 2017 ) represent pairs of genomic coordinates, they are not designed for representing structural variants in a manner that supports ease of integration with existing Bioconductor annotation and analysis packages, the ability to compare equivalent structural variants regardless of the VCF notation the variant is represented in, nor do they support single breakend variants ( Cameron et al , 2021 ). StructuralVariantAnnotation takes the approach of decomposing all structural variant calls into their constituent breakpoints.…”

Section: Breakpoint-centric Design Philosophymentioning

confidence: 99%

StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem

2022

Self Cite

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Summary StructuralVariantAnnotation is an R/Bioconductor package that provides a framework for decoupling downstream analysis of structural variant breakpoints from upstream variant calling methods. It standardizes the representational format from BEDPE, or any of the three different notations supported by VCF into a breakpoint GRanges data structure suitable for use by the wider Bioconductor ecosystem. It handles both transitive breakpoints and duplication/insertion notational differences of identical variants—both common scenarios when comparing short/long read-based call sets that confound downstream analysis. StructuralVariantAnnotation provides the caller-agnostic foundation needed for a R/Bioconductor ecosystem of structural variant annotation, classification and interpretation tools able to handle both simple and complex genomic rearrangements. Availability and implementation StructuralVariantAnnotation is implemented in R and available for download as the Bioconductor StructuralVariantAnnotation package. Details can be found at https://www.bioconductor.org/packages/release/bioc/html/StructuralVariantAnnotation.html. It has been released under a GPL license.

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“…GRIDSS2 and its companion interpreter tool LINX were employed for somatic structural variant analysis and Gene fusion [35]. COSMIC3 based SNVs and Indels signatures from the whole genome were built using MutationalPatterns [36] software, respectively.…”

Section: Methodsmentioning

confidence: 99%

Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma

Thind

Ashford

Strbenac

et al. 2022

Preprint

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Metastatic cutaneous squamous cell carcinoma (cSCC) is associated with a high risk of recurrence and poor prognosis. There is limited published data exploring whole genome sequencing (WGS). The aim of this project was to provide the first comprehensive genomic understanding of the state of metastatic cSCC. In this study, we used WGS on matched tumor and blood DNA to detect somatic genetic alterations from 25 patients with regional metastases of head and neck cSCC. Our computational analyses interrogate clinical impacts of these genetic alterations on metastatic cSCC across the cohort for both the coding and non-coding genome. In the non-coding genome, 3UTR regions of EVC (48%), PPP1R1A (48%) and LUM (16%) were significantly functionally altered (Q-value < 0.05). Further, significant functional alterations are observed in the tumor suppressing lncRNA LINC01003 ( 68% of specimens, Q-value: 0.0158). In addition, significant recurrent copy number loss in tumor suppressor genes KANSL1 and PTPRD and gain in CALR, CCND1 and FGF3 was observed for coding regions. SNVs driver analyses predicted TP53, CDKN2A, as potential drivers of the metastasis cSCC (using 3 different tools). Indel signature analysis highlight dominance of ID signature 13 followed by ID8 & ID9. Interestingly, ID 9 has previously been shown to have no association with skin melanoma, unlike ID 13 and 8, suggesting some point of difference between these two skin-based diseases. The overall landscape of variation in metastatic cSCC is dominated by cell cycle and DNA repair disruption.

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GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

Cited by 104 publications

References 39 publications

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

StructuralVariantAnnotation: a R/Bioconductor foundation for a caller-agnostic structural variant software ecosystem

Whole genome analysis reveals the genomic complexity in metastatic cutaneous squamous cell carcinoma

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