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01895nam a2200325Ia 4500 |
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10.1186-s13073-022-01046-6 |
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220510s2022 CNT 000 0 und d |
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|a 1756994X (ISSN)
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|a SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing
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|b BioMed Central Ltd
|c 2022
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|z View Fulltext in Publisher
|u https://doi.org/10.1186/s13073-022-01046-6
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|a Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnna. © 2022, The Author(s).
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|a Long-read sequencing
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|a Structural variant
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|a Whole genome sequencing
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|a Balachandran, P.
|e author
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|a Beck, C.R.
|e author
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|a Danis, D.
|e author
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|a Haimel, M.
|e author
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|a Helbig, I.
|e author
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|a Jacobsen, J.O.B.
|e author
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|a Lee, C.
|e author
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|a Lyon, G.J.
|e author
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|a Mungall, C.J.
|e author
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|a Reese, J.
|e author
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|a Robinson, P.N.
|e author
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|a Smedley, D.
|e author
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|a Yilmaz, F.
|e author
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|a Zhu, Q.
|e author
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|t Genome Medicine
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