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

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing.
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DOI:
10.1186/s13073-022-01046-6
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发表时间:
2022-04-28
期刊:
影响因子:
12.3
通讯作者:
--
中科院分区:
生物学1区
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--
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结构变异体(SV)与孟德尔疾病的病因有关,但由于测序技术的限制,系统性地未被充分确定。长读段测序使得能够全面检测SV,但是需要用于对候选SV进行优先排序的方法。结构变异注释和分析(SvAnna)评估所有类别的SV及其与转录本和调控序列的交叉,将预测的对基因功能的影响与临床表型数据联系起来。SvAnna将87%的有害SV排在前十名。SvAnna提供的可解释的优先顺序将促进诊断基因组学中长读序测序的广泛采用。SvAnna可在https://github.com/TheJacksonLaboratory/SvAnna上找到。在线版本包含补充材料,可通过10.1186/s13073-022-01046-6获得。
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. The online version contains supplementary material available at 10.1186/s13073-022-01046-6.
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