KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium ; IBBT Future Health Department, Kasteelpark Arenberg 10, B-3001, Leuven, Belgium.
KU Leuven, Centre for Human Genetics, University Hospital Gasthuisberg, Herestraat 49, 3000 Leuven, Belgium.
Genome Med. 2012 Sep 26;4(9):73. doi: 10.1186/gm374. eCollection 2012.
The increasing size and complexity of exome/genome sequencing data requires new tools for clinical geneticists to discover disease-causing variants. Bottlenecks in identifying the causative variation include poor cross-sample querying, constantly changing functional annotation and not considering existing knowledge concerning the phenotype. We describe a methodology that facilitates exploration of patient sequencing data towards identification of causal variants under different genetic hypotheses. Annotate-it facilitates handling, analysis and interpretation of high-throughput single nucleotide variant data. We demonstrate our strategy using three case studies. Annotate-it is freely available and test data are accessible to all users at http://www.annotate-it.org.
外显子/基因组测序数据的规模和复杂性不断增加,这就需要临床遗传学家使用新工具来发现致病变异。在确定致病变异时存在一些瓶颈,包括跨样本查询效果不佳、功能注释不断变化以及没有考虑与表型相关的现有知识。我们描述了一种方法,该方法可促进在不同遗传假设下探索患者测序数据,以识别因果变异。Annotate-it 有助于处理、分析和解释高通量单核苷酸变异数据。我们使用三个案例研究来演示我们的策略。Annotate-it 是免费提供的,所有用户都可以在 http://www.annotate-it.org 上访问测试数据。