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Question: Methods of classifying, not detecting, structural variation?
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I have completed a genome-wide scan for structural variants in genomic loci of interest for my disease state.

As expected, this scan identified high-confidence variants from several loci known to be rich in structural variation and copy number variation based on read depth information.

Some of these variants are similar, but not identical to, variants annotated in dbVar or ClinVar. In fact, a few are so similar I expect it is possible they are even the same variant. Even if they are not identical, to the degree they resemble known variants, the phenotypes they produce may in fact be similar to the effect of known variants whose phenotype has been described.

However, when I search for existing tools to identify how similar a variant I have is to a known, characterized variant, all I seem to be able to turn up is algorithms that _detect_ SVs, which is not what I am after (e.g. the Breakdancer algorithm and other such manuscripts will come up on Pubmed or Google Searches). I have identified several papers, such as this one,, but they tend to be older.

Is there a modern, up to date, SV and CNV annotator and comparator (tool)? Or is there a relatively recent review paper I should start by reading?

Thank you very much.

ADD COMMENTlink 4.2 years ago Vincent Laufer ♦ 1.0k • updated 2.6 years ago Biostar 20
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For breakpoint precise structural variants you may want to try the left-alignment of (Pubmed ID: 25701572).

ADD REPLYlink 4.2 years ago
♦ 1.3k
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  • To identify how similar a structural variant (SV) is to a known variant from DGV, 1000g or DECIPHER, I suggest using AnnotSV. You can look at this post describing the annotSV tool: Annotation for SV and CNV

  • Else, if you want to identify similar SV detected from different callers, I advise you to merge SV that share a 70% reciprocal overlap measured by length and position (> 70% shared length) (as done in DGV).

ADD REPLYlink 19 months ago
• 90
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A nice formal framework to compare deletions was introduced by Wittler et al. (Pubmed PMID: 25979471) but I haven't tested their software implementation.

ADD COMMENTlink 4.2 years ago trausch ♦ 1.3k

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