InvFEST, a database integrating information of polymorphic inversions in the human genome

Martínez-Fundichely, Alexander; Casillas, Sònia; Egea, Raquel; Ràmia, Miquel; Barbadilla, Antonio; Pantano, Lorena; Puig, Marta; Cáceres, Mario

doi:10.1093/nar/gkt1122

Cited by 46 publications

(93 citation statements)

References 36 publications

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“…This resolution coincided very closely with previous reports, including those listed in the Database of Genomic Variants (DGV) (MacDonald et al 2014), and the Human Polymorphic Inversion Database (InvFest) (Fig. 2B, iv;Martinez-Fundichely et al 2014), highlighting how inversion breakpoints can be accurately mapped using Invert.R.…”

Section: Resultssupporting

confidence: 89%

“…1E, ii, red bars). These coordinates coincide with previous reports of each inversion Antonacci et al 2009;Martinez-Fundichely et al 2014) (also see Fig. 2B), demonstrating how visualizing changes in strand orientation in chromosomes allows us to discover, map, and genotype inversions at high resolution in a single cell.…”

Section: Resultssupporting

confidence: 87%

“…2B, i, to the manual breakpoints of 7,224,417,334 in Fig. 1E, ii) as well as previous reports Antonacci et al 2009;Martinez-Fundichely et al 2014). To define the limits of detection of Invert.R, we randomly down-sampled a singlecell library and tested the size range of inversions reliably predicted using our pipeline (Supplemental Fig.…”

Section: Resultsmentioning

confidence: 69%

“…This strategy offers far greater efficiency (in terms of time, cost, resolution, and sensitivity) compared to alternative HTS approaches that require deep genomic coverage (up to 135×) to discover inversions (Tuzun et al 2005;Bansal et al 2007;Korbel et al 2007;Kidd et al 2008;Sharp et al 2008;Ahn et al 2009;Pang et al 2010). Indeed, we characterized variants smaller than 1 kb and up to 4.5 Mb, (matching or surpassing the sensitivity of other studies [Tuzun et al 2005;Bansal et al 2007;Kidd et al 2010;Martinez-Fundichely et al 2014;Sudmant et al 2015]). We also discovered several new inversions not previously described, many of which were within blocks of segmental duplications where variant mapping has previously been challenging (Feuk 2010;Alkan et al 2011;Alves et al 2012;Martinez-Fundichely et al 2014).…”

Section: Genome Research 1583mentioning

confidence: 96%

“…Indeed, polymorphic rearrangements are a common feature of the human genome (Pang et al 2010) and are implicated in speciation (Feuk et al 2005;Zody et al 2008), population diversification (Stefansson et al 2005;Alves et al 2014), and many complex diseases, including neurological disorders and cancers (Antonarakis et al 1995;Bondeson et al 1995;Koolen et al 2006;Shaw-Smith et al 2006;Sharp et al 2008;Tam et al 2008;Antonacci et al 2009;Salm et al 2012). However, few human inversions have been studied comprehensively to date, and the phenotypic consequences and clinical relevance of most remain undefined (Feuk 2010;Alkan et al 2011;Alves et al 2012;Martinez-Fundichely et al 2014).…”

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confidence: 99%

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Characterizing polymorphic inversions in human genomes by single-cell sequencing

et al. 2016

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Identifying genomic features that differ between individuals and cells can help uncover the functional variants that drive phenotypes and disease susceptibilities. For this, single-cell studies are paramount, as it becomes increasingly clear that the contribution of rare but functional cellular subpopulations is important for disease prognosis, management, and progression. Until now, studying these associations has been challenged by our inability to map structural rearrangements accurately and comprehensively. To overcome this, we coupled single-cell sequencing of DNA template strands (Strand-seq) with custom analysis software to rapidly discover, map, and genotype genomic rearrangements at high resolution. This allowed us to explore the distribution and frequency of inversions in a heterogeneous cell population, identify several polymorphic domains in complex regions of the genome, and locate rare alleles in the reference assembly. We then mapped the entire genomic complement of inversions within two unrelated individuals to characterize their distinct inversion profiles and built a nonredundant global reference of structural rearrangements in the human genome. The work described here provides a powerful new framework to study structural variation and genomic heterogeneity in single-cell samples, whether from individuals for population studies or tissue types for biomarker discovery.

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Section: Resultssupporting

confidence: 89%

Section: Resultssupporting

confidence: 87%

Section: Resultsmentioning

confidence: 69%

Section: Genome Research 1583mentioning

confidence: 96%

mentioning

confidence: 99%

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Characterizing polymorphic inversions in human genomes by single-cell sequencing

et al. 2016

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Inversions on human chromosomes

Kosuthova

Šolc

2022

American J of Med Genetics Pt A

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Human chromosome inversions are types of balanced structural variations, making them difficult to analyze. Thanks to PEM (paired‐end sequencing and mapping), there has been tremendous progress in studying inversions. Inversions play an important role as an evolutionary factor, contributing to the formation of gonosomes, speciation of chimpanzees and humans, and inv17q21.3 or inv8p23.1 exhibit the features of natural selection. Both inversions have been related to pathogenic phenotype by directly affecting a gene structure (e.g., inv5p15.1q14.1), regulating gene expression (e.g., inv7q21.3q35) and by predisposing to other secondary arrangements (e.g., inv7q11.23). A polymorphism of human inversions is documented by the InvFEST database (a database that stores information about clinical predictions, validations, frequency of inversions, etc.), but only a small fraction of these inversions is validated, and a detailed analysis is complicated by the frequent location of breakpoints within regions of repetitive sequences.

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Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

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Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

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InvFEST, a database integrating information of polymorphic inversions in the human genome

Cited by 46 publications

References 36 publications

Characterizing polymorphic inversions in human genomes by single-cell sequencing

Characterizing polymorphic inversions in human genomes by single-cell sequencing

Inversions on human chromosomes

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

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