Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.
Annotation of structural variants with reported allele frequencies and related metrics from multiple datasets using SVAFotate.
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DOI：
10.1186/s12859-022-05008-y
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发表时间：
2022-11-16
影响因子：
3
通讯作者：
Quinlan, Aaron R.
中科院分区：
文献类型：
--
作者：
Nicholas, Thomas J.;Cormier, Michael J.;Quinlan, Aaron R.
使用DNA测序数据鉴定有害的遗传变异依赖于越来越详细的过滤策略，以分离更可能成为疾病表型基础的变异的小子集。反映不同类型变异的群体等位基因频率的数据集可作为强大的过滤工具，特别是在罕见疾病分析的背景下。虽然这种群体规模的等位基因频率数据集现在存在于结构变体（SV）中，但在多个数据集之间匹配SV调用仍然是一个挑战，从而使推定SV的群体等位基因频率的估计复杂化。我们介绍SVAFotate，一个软件工具，使注释的SV变异等位基因频率和相关信息，从现有的SV数据集。因此，由SVAFotate注释的VCF文件提供了各种指标，以帮助将SV分层为更广泛人群中的常见或罕见。在这里，我们展示了SVAFotate在SV分类中的使用，以及它们的群体频率，并说明了如何使用SVAFotate的注释来过滤和优先考虑SV。最后，我们详细介绍了如何最好地利用这些SV注释的遗传变异的研究罕见疾病的分析。 在线版本包含补充材料，可在10.1186/s12859-022-05008-y获得。
Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease. The online version contains supplementary material available at 10.1186/s12859-022-05008-y.
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影响因子：
12.3
作者：
McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
通讯作者：
Cunningham F
影响因子：
5.3
作者：
Pedersen BS;Brown JM;Dashnow H;Wallace AD;Velinder M;Tristani-Firouzi M;Schiffman JD;Tvrdik T;Mao R;Best DH;Bayrak-Toydemir P;Quinlan AR
通讯作者：
Quinlan AR
影响因子：
64.8
作者：
Sudmant PH;Rausch T;Gardner EJ;Handsaker RE;Abyzov A;Huddleston J;Zhang Y;Ye K;Jun G;Fritz MH;Konkel MK;Malhotra A;Stütz AM;Shi X;Casale FP;Chen J;Hormozdiari F;Dayama G;Chen K;Malig M;Chaisson MJP;Walter K;Meiers S;Kashin S;Garrison E;Auton A;Lam HYK;Mu XJ;Alkan C;Antaki D;Bae T;Cerveira E;Chines P;Chong Z;Clarke L;Dal E;Ding L;Emery S;Fan X;Gujral M;Kahveci F;Kidd JM;Kong Y;Lameijer EW;McCarthy S;Flicek P;Gibbs RA;Marth G;Mason CE;Menelaou A;Muzny DM;Nelson BJ;Noor A;Parrish NF;Pendleton M;Quitadamo A;Raeder B;Schadt EE;Romanovitch M;Schlattl A;Sebra R;Shabalin AA;Untergasser A;Walker JA;Wang M;Yu F;Zhang C;Zhang J;Zheng-Bradley X;Zhou W;Zichner T;Sebat J;Batzer MA;McCarroll SA;1000 Genomes Project Consortium;Mills RE;Gerstein MB;Bashir A;Stegle O;Devine SE;Lee C;Eichler EE;Korbel JO
通讯作者：
Korbel JO
影响因子：
4.4
作者：
Samarakoon PS;Sorte HS;Stray-Pedersen A;Rødningen OK;Rognes T;Lyle R
通讯作者：
Lyle R
影响因子：
1.2
作者：
Cingolani, Pablo;Platts, Adrian;Ruden, Douglas M.
通讯作者：
Ruden, Douglas M.