In Silico Prediction of Deleteriousness for Nonsynonymous and Splice-Altering Single Nucleotide Variants in the Human Genome
In Silico Prediction of Deleteriousness for Nonsynonymous and Splice-Altering Single Nucleotide Variants in the Human Genome
复制标题
DOI：
10.1007/978-1-4939-6472-7_13
复制
发表时间：
2017-01-01
期刊：
影响因子：
--
通讯作者：
Liu, Xiaoming
中科院分区：
文献类型：
--
作者：
Jian, Xueqiu;Liu, Xiaoming
计算机预测方法在分子生物学，特别是人类遗传学中越来越有价值和流行，用于通过人类基因组测序来筛选和优先考虑大量DNA变异的有害预测。关于DNA变异如何影响基因表达，在知识的不同层次/方面开发了丰富的可用方法。考虑到他们的预测并不总是一致的，甚至与预期相反，在这些方法中使用共识预测或多数投票比相信任何一种方法更可取。因为对于这样的大数据集，以不同的方法查询不同的数据库既繁琐又耗时，所以一个集成了来自多个数据库的预测的数据库可以简化这一过程。在本章中，我们描述了从dbNSFP中获得大量变体的综合预测和注释的一般步骤，dbNSFP是同类数据库中第一个，可能也是最广泛使用的数据库。
In silico prediction methods have increasingly been valuable and popular in molecular biology, especially in human genetics, for deleteriousness prediction to filter and prioritize huge amounts of DNA variation identified by sequencing human genomes. There is a rich collection of available methods developed upon different levels/aspects of knowledge about how DNA variations affect gene expression. Given the fact that their predictions are not always consistent or even opposite of what was expected, using consensus prediction or majority vote among these methods is preferred to trusting any single one. Because querying different databases for different methods is both tedious and time-consuming for such big data sets, one database integrating predictions from multiple databases can facilitate the process. In this chapter, we describe the general steps of obtaining comprehensive predictions and annotations for large numbers of variants from dbNSFP, the first and probably the most widely used database of its kind.