SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

Daniš, Daniel; Jacobsen, Julius O. B.; Balachandran, Parithi; Zhu, Qihui; Yilmaz, Feyza; Reese, Justin; Haimel, Matthias; Lyon, Gholson J.; Helbig, Ingo; Mungall, Christopher J.; Beck, Christine R.; Lee, Charles; Smedley, Damian; Robinson, Peter N.

doi:10.1186/s13073-022-01046-6

Cited by 13 publications

(10 citation statements)

References 53 publications

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“…These different types of SV will need to be accounted for when predicting their effects. Several strategies to predict SV effects in humans have deployed existing tools to predict the biological effects of individual bases spanning the SV [ 75 – 77 ]. Theoretically, this strategy can also be applied to livestock species.…”

Section: Validation Of Sv Effects and Evaluating Their Role In Molecu...mentioning

confidence: 99%

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants

et al. 2023

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Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.

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Section: Validation Of Sv Effects and Evaluating Their Role In Molecu...mentioning

confidence: 99%

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants

et al. 2023

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“…They were then filtered and analyzed using in-house software that is also used for srGS data (Hiatt et al 2021). Rare SVs were assessed by visualization of reads in IGV and prioritization and analysis using SvAnna (Danis et al 2022). All variants of interest were subject to curation using American College of Medical Genetics and Genomics and Association for Molecular Pathology (ACMG/AMP) and ClinGen criteria to identify potentially clinically relevant variation (Richards et al 2015; Riggs et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Section: Findings From Lrgsmentioning

confidence: 99%

Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders

Hiatt,

Lawlor,

Handley

et al. 2024

Preprint

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Variant detection from long-read genome sequencing (lrGS) has proven to be considerably more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare disease that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, eight of which (8/96, 8.33%) harbored pathogenic or likely pathogenic variants. Newly identified variants were visible in both srGS and lrGS in nine probands (~9.4%) and resulted from changes to interpretation mostly from recent gene-disease association discoveries. Seven cases included variants that were only interpretable in lrGS, including copy-number variants, an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either: not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality-control and filtration. Thus, while reanalysis of older data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS datasets grow allowing for better variant frequency annotation, the additional lrGS-only rare disease yield will grow over time.

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“…Thanks to the ACMG/ClinGen guidelines, SV ranking is based on a quantitative scoring framework. However, several methods have been proposed to assess SV pathogenicity such as CADD-SV ( 33 ), DeepSVP ( 34 ), StrVCTVRE ( 35 ) or SvAnna ( 36 ). AnnotSV is one of the most comprehensive tool available for annotation and prioritization of human SV ( Supplementary Table S4 ).…”

Section: Resultsmentioning

confidence: 99%

The AnnotSV webserver in 2023: updated visualization and ranking

Geoffroy,

Lamouche,

Guignard

et al. 2023

Nucleic Acids Research

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Much of the human genetics variant repertoire is composed of single nucleotide variants (SNV) and small insertion/deletions (indel) but structural variants (SV) remain a major part of our modified DNA. SV detection has often been a complex question to answer either because of the necessity to use different technologies (array CGH, SNP array, Karyotype, Optical Genome Mapping…) to detect each category of SV or to get an appropriate resolution (Whole Genome Sequencing). Thanks to the deluge of pangenomic analysis, Human geneticists are accumulating SV and their interpretation remains time consuming and challenging. The AnnotSV webserver (https://www.lbgi.fr/AnnotSV/) aims at being an efficient tool to (i) annotate and interpret SV potential pathogenicity in the context of human diseases, (ii) recognize potential false positive variants from all the SV identified and (iii) visualize the patient variants repertoire. The most recent developments in the AnnotSV webserver are: (i) updated annotations sources and ranking, (ii) three novel output formats to allow diverse utilization (analysis, pipelines), as well as (iii) two novel user interfaces including an interactive circos view.

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SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

Cited by 13 publications

References 53 publications

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants

Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders

The AnnotSV webserver in 2023: updated visualization and ranking

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