A robust benchmark for detection of germline large deletions and insertions.
A robust benchmark for detection of germline large deletions and insertions.
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DOI：
10.1038/s41587-020-0538-8
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发表时间：
2020-11
影响因子：
46.9
通讯作者：
Salit M
中科院分区：
文献类型：
--
作者：
Zook JM;Hansen NF;Olson ND;Chapman L;Mullikin JC;Xiao C;Sherry S;Koren S;Phillippy AM;Boutros PC;Sahraeian SME;Huang V;Rouette A;Alexander N;Mason CE;Hajirasouliha I;Ricketts C;Lee J;Tearle R;Fiddes IT;Barrio AM;Wala J;Carroll A;Ghaffari N;Rodriguez OL;Bashir A;Jackman S;Farrell JJ;Wenger AM;Alkan C;Soylev A;Schatz MC;Garg S;Church G;Marschall T;Chen K;Fan X;English AC;Rosenfeld JA;Zhou W;Mills RE;Sage JM;Davis JR;Kaiser MD;Oliver JS;Catalano AP;Chaisson MJP;Spies N;Sedlazeck FJ;Salit M
新的技术和分析方法使基因组结构变异（SVs）的检测精度、分辨率和全面性不断提高。为了帮助将这些方法转化为常规研究和临床实践，我们开发了第一个序列分辨基准集，用于识别假阴性和假阳性的种系大插入和缺失。为了在广泛可用的细胞和DNA的个人基因组计划三人组中为广泛同意的子创建这个基准，瓶中基因组（GIAB）联盟整合了来自不同技术的19种序列解析变体调用方法。最终的基准集包含12745个分离的，序列分辨的插入（7281）和删除（5464）调用≥50个碱基对（bp）。1级基准区域，任何额外的呼叫都是假定的假阳性，覆盖2.51 Gbp和5262个插入和4095个删除，由≥1个二倍体组装支持。我们证明了该基准集可靠地识别来自短读、链读和长读测序和光学映射的高质量SV调用集的假阴性和假阳性。
New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed the first sequence-resolved benchmark set for identification of both false negative and false positive germline large insertions and deletions. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12745 isolated, sequence-resolved insertion (7281) and deletion (5464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5262 insertions and 4095 deletions supported by ≥1 diploid assembly. We demonstrate the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.
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影响因子：
12.3
作者：
Barseghyan H;Tang W;Wang RT;Almalvez M;Segura E;Bramble MS;Lipson A;Douine ED;Lee H;Délot EC;Nelson SF;Vilain E
通讯作者：
Vilain E
影响因子：
7
作者：
Huddleston J;Chaisson MJP;Steinberg KM;Warren W;Hoekzema K;Gordon D;Graves-Lindsay TA;Munson KM;Kronenberg ZN;Vives L;Peluso P;Boitano M;Chin CS;Korlach J;Wilson RK;Eichler EE
通讯作者：
Eichler EE
影响因子：
46.9
作者：
通讯作者：
--
影响因子：
12.3
作者：
Lee AY;Ewing AD;Ellrott K;Hu Y;Houlahan KE;Bare JC;Espiritu SMG;Huang V;Dang K;Chong Z;Caloian C;Yamaguchi TN;ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants;Kellen MR;Chen K;Norman TC;Friend SH;Guinney J;Stolovitzky G;Haussler D;Margolin AA;Stuart JM;Boutros PC
通讯作者：
Boutros PC
影响因子：
7
作者：
Marks, Patrick;Garcia, Sarah;Church, Deanna M.
通讯作者：
Church, Deanna M.