Application of BIG-Annotator in the genome sequencing data functional annotation and genetic diagnosis
Received date: 2018-09-29
Revised date: 2018-11-02
Online published: 2018-11-06
Supported by
Supported by the National Natural Science Foundation of China(31571370);Supported by the National Natural Science Foundation of China(91631106);the “Hundred Talents Program” of Chinese Academy of Sciences
The next generation sequencing (NGS) technology has been playing important roles in genetic diagnosis of diseases in recent years, and serving as a technological basis of precision medicine. In analyzing NGS data, the variant annotation is an important step. In this study, we developed a computationally efficient software (BIG-Annotator) to perform functional annotation for whole-genome single nucleotide polymorphisms. BIG-Annotator integrates the widely used databases and pipelines for variant annotation of genetic diseases and tumors, and follows the 2015 ACMG-AMP Standard Guide for Interpretation and Reporting of Clinical Variants. BIG-Annotator is ten times faster than the existing software, and suitable for annotating genomic sequencing data from large samples. Here we present two analysis cases of genetic diagnosis using BIG-Annotator to show its applications.
Key words: NGS; variant annotation; genetic diagnosis; precision medicine
Ying Huang,Qi Liu,Lianjiang Chi,Chengmin Shi,Zhen Wu,Min Hu,Hong Shi,Hua Chen . Application of BIG-Annotator in the genome sequencing data functional annotation and genetic diagnosis[J]. Hereditas(Beijing), 2018 , 40(11) : 1015 -1023 . DOI: 10.16288/j.yczz.18-274
| [1] | Mirnezami R, Nicholson J, Darzi A . Preparing for precision medicine. N Engl J Med, 2012,366(6):489-491. |
| [2] | Middha S. Bioinformatics solution for clinical utilization of next generation DNA sequencing[Dissertation]. 2014, . |
| [3] | Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res, 2010,38(16):e164. |
| [4] | Li Q, Wang K . InterVar: clinical interpretation of genetic variants by the 2015 ACMG-AMP guidelines. Am J Hum Genet, 2017,100(2):267-280. |
| [5] | Altshuler D . A map of human genome variation from population-scale sequencing. Nature, 2010,467(7319):1061-1073. |
| [6] | ACMG Board of Directors . ACMG policy statement: updated recommendations regarding analysis and reporting of secondary findings in clinical genome-scale sequencing. Genet Med, 2015,17(1):68-69. |
| [7] | Landrum MJ, Lee JM, Riley GR, Jang E, Rubinstein WS, Church DM, Maglott DR . ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res, 2014,42(Database issue):D980-985. |
| [8] | Krawczak M, Ball EV, Fenton I, Stenson PD, Abeysinghe S, Thomas N, Cooper DN . Human gene mutation database-a biomedical information and research resource. Hum Mutat, 2015,15(1):45-51. |
| [9] | Slavin TP, Van Tongeren LR, Behrendt CE, Solomon I, Ryback C, Nehoray B, Kuzmich L, Niell-Swiller M, Blazer KR, Tao S, Yang K, Culver JO, Sand S, Castillo D, Herzog J, Gray SW, Weitzel JN . Prospective study of cancer genetic variants: variation in rate of reclassification by ancestry. J Natl Cancer Inst, 2018,110(10):1059-1069. |
| [10] | Monti E, Mottes M, Fraschini P, Brunelli P, Forlino A, Venturi G, Doro F, Perlin S, Cavarzere P, Antoniazzi F . Current and emerging treatments for the management of osteogenesis imperfecta. Ther Clin Risk Manag, 2010,6(2):367-381. |
| [11] | Kataoka K, Ogura E, Hasegawa K, Inoue M, Seino Y, Morishima T, Tanaka H . Mutations in type I collagen genes in Japanese osteogenesis imperfecta patients. Pediatr Int, 2010,49(5):564-569. |
/
| 〈 |
|
〉 |