小柯机器人

美国加州大学Joseph G. Gleeson和Xiaoxu Yang共同合作，近期取得重要工作进展。他们研究提出了基于DeepMosaic工具，来检测不受控制的嵌合单核苷酸变异。相关论文2023年1月2日在线发表于《自然—生物技术》杂志上。

据介绍，嵌合变异（MV）反映了胚胎发育和环境暴露期间的突变过程，随着年龄增长而积累，并成为癌症和自闭症等疾病的基础。由于非克隆扩增MV的稀疏表征，非癌MV的检测在计算上具有挑战性。

研究人员介绍了DeepMosaic工具，它结合了用于单核苷酸MV的基于图像可视化模块和用于控制无关MV检测的基于卷积神经网络的分类模块。DeepMosaic对180000个模拟或实验评估的MV进行了训练，并对来自16个基因组和181个外显子的619740个模拟MV和530个独立的生物测试MV进行了基准测试。与现有方法相比，DeepMosaic在生物数据上实现了更高的准确性，对非癌全基因组测序数据的敏感性为0.78，特异性为0.83，阳性预测值为0.96，

此外，在非癌症全外显子组测序数据上，验证率比以前的最佳实践方法提高了一倍（0.43比0.18）。因此，DeepMosaic代表了一种用于非癌症样本的准确MV分类器，可以作为现有方法的替代或补充。

附：英文原文

Title: Control-independent mosaic single nucleotide variant detection with DeepMosaic

Author: Yang, Xiaoxu, Xu, Xin, Breuss, Martin W., Antaki, Danny, Ball, Laurel L., Chung, Changuk, Shen, Jiawei, Li, Chen, George, Renee D., Wang, Yifan, Bae, Taejeong, Cheng, Yuhe, Abyzov, Alexej, Wei, Liping, Alexandrov, Ludmil B., Sebat, Jonathan L., Gleeson, Joseph G.

Issue&Volume: 2023-01-02

Abstract: Mosaic variants (MVs) reflect mutagenic processes during embryonic development and environmental exposure, accumulate with aging and underlie diseases such as cancer and autism. The detection of noncancer MVs has been computationally challenging due to the sparse representation of nonclonally expanded MVs. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs and a convolutional neural network-based classification module for control-independent MV detection. DeepMosaic was trained on 180,000 simulated or experimentally assessed MVs, and was benchmarked on 619,740 simulated MVs and 530 independent biologically tested MVs from 16 genomes and 181 exomes. DeepMosaic achieved higher accuracy compared with existing methods on biological data, with a sensitivity of 0.78, specificity of 0.83 and positive predictive value of 0.96 on noncancer whole-genome sequencing data, as well as doubling the validation rate over previous best-practice methods on noncancer whole-exome sequencing data (0.43 versus 0.18). DeepMosaic represents an accurate MV classifier for noncancer samples that can be implemented as an alternative or complement to existing methods.

DOI: 10.1038/s41587-022-01559-w

Source: https://www.nature.com/articles/s41587-022-01559-w

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex