Department of Integrative Biology, University of California, Berkeley, CA 94720, USA.
Nat Rev Genet. 2011 Jun;12(6):443-51. doi: 10.1038/nrg2986.
Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes. Recently developed statistical methods both improve and quantify the considerable uncertainty associated with genotype calling, and will especially benefit the growing number of studies using low- to medium-coverage data. We review these methods and provide a guide for their use in NGS studies.
对下一代测序（NGS）数据的有意义分析，这些数据是由遗传学和基因组学研究广泛产生的，关键依赖于 SNP 和基因型的准确调用。最近开发的统计方法既改进又量化了与基因型调用相关的相当大的不确定性，并且尤其将使越来越多使用低至中等覆盖数据的研究受益。我们回顾这些方法，并为它们在 NGS 研究中的使用提供指南。
