Department of Genetics and Pharmacogenomics, MRL, Boston, MA, USA.
Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Genome Biol. 2018 Oct 25;19(1):173. doi: 10.1186/s13059-018-1546-6.
Functional characterization of the noncoding genome is essential for biological understanding of gene regulation and disease. Here, we introduce the computational framework PINES (Phenotype-Informed Noncoding Element Scoring), which predicts the functional impact of noncoding variants by integrating epigenetic annotations in a phenotype-dependent manner. PINES enables analyses to be customized towards genomic annotations from cell types of the highest relevance given the phenotype of interest. We illustrate that PINES identifies functional noncoding variation more accurately than methods that do not use phenotype-weighted knowledge, while at the same time being flexible and easy to use via a dedicated web portal.
功能基因组学的研究对于理解基因调控和疾病至关重要。在这里，我们介绍了一个名为 PINES（表型信息指导的非编码元件评分）的计算框架，它通过表型依赖的方式整合表观遗传注释来预测非编码变异的功能影响。PINES 可以根据感兴趣的表型，针对与目标细胞类型最相关的基因组注释进行定制化分析。我们的研究表明，与不使用表型加权知识的方法相比，PINES 能够更准确地识别功能非编码变异，同时通过专用的网络门户，它还具有灵活性和易用性。
