Department of Bioengineering, University of California, Berkeley, 94720, USA.
Genome Biol. 2021 Jan 19;22(1):38. doi: 10.1186/s13059-020-02255-1.
We develop a general computational approach for improving the accuracy of basecalling with Oxford Nanopore's 1D and related sequencing protocols. Our software PoreOver ( https://github.com/jordisr/poreover ) finds the consensus of two neural networks by aligning their probability profiles, and is compatible with multiple nanopore basecallers. When applied to the recently-released Bonito basecaller, our method reduces the median sequencing error by more than half.
我们开发了一种通用的计算方法，用于提高牛津纳米孔的 1D 和相关测序协议的碱基准确率。我们的软件 PoreOver（https://github.com/jordisr/poreover）通过对齐它们的概率分布来找到两个神经网络的共识，并与多个纳米孔碱基识别器兼容。当应用于最近发布的 Bonito 碱基识别器时，我们的方法将测序错误的中位数降低了一半以上。
