Bhardwaj Vinnu, Pevzner Pavel A, Rashtchian Cyrus, Safonova Yana
Electrical and Computer Engineering Department, University of California San Diego, La Jolla, USA.
Computer Science and Engineering Department, University of California San Diego, La Jolla, USA.
IEEE Trans Inf Theory. 2021 Jun;67(6):3295-3314. doi: 10.1109/tit.2020.3030569. Epub 2020 Oct 13.
The problem of reconstructing a string from its error-prone copies, , was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.
从易出错的副本中重建字符串的问题,是弗拉基米尔·列文斯坦在二十年前提出的。虽然在迹线重建方面已经有了大量的理论工作,但实际解决方案直到最近才在两个快速发展的研究领域中出现:免疫基因组学和DNA数据存储。在免疫基因组学中,迹线对应于基因的突变副本,这些突变是由适应性免疫系统自然产生的。在DNA数据存储中,迹线对应于编码数字数据的DNA分子的噪声副本,错误是数据检索过程中的人为产物。在本文中,我们介绍了几种新的迹线生成模型以及与免疫基因组学和DNA数据存储中的迹线重建相关的开放性问题,综述了迹线重建的理论结果,并强调了它们与计算生物学的联系。在整个过程中,我们讨论了已知解决方案的适用性和缺点,并提出了未来的研究方向。