1] Program in Bioinformatics, Boston University, Massachusetts 02115, USA.Department of Genetics, Harvard Medical School, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, USA. Present address: Expression Analysis, a Quintiles Company, Durham, North Carolina 27713, USA. [2].
1] Department of Genetics, Harvard Medical School, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts 02115, USA. Department of Biology, Brigham Young University, Provo, Utah 84602, USA. Present address: Division of Pediatric Pharmacology and Drug Discovery, University of California, San Diego School of Medicine, La Jolla, California 92093, USA. [2].
Nat Rev Genet. 2014 Jan;15(1):56-62. doi: 10.1038/nrg3655. Epub 2013 Dec 10.
Advances in next-generation sequencing (NGS) technologies have rapidly improved sequencing fidelity and substantially decreased sequencing error rates. However, given that there are billions of nucleotides in a human genome, even low experimental error rates yield many errors in variant calls. Erroneous variants can mimic true somatic and rare variants, thus requiring costly confirmatory experiments to minimize the number of false positives. Here, we discuss sources of experimental errors in NGS and how replicates can be used to abate such errors.
下一代测序 (NGS) 技术的进步极大地提高了测序的准确性，并显著降低了测序错误率。然而，考虑到人基因组中有数十亿个核苷酸，即使实验错误率很低，也会在变异调用中产生许多错误。错误的变异可能模拟真实的体细胞和罕见变异，因此需要进行昂贵的确认实验来尽量减少假阳性的数量。在这里，我们讨论了 NGS 中的实验误差源，以及如何使用重复样本来减轻这些误差。
