Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia.
St Vincent's Clinical School, Faculty of Medicine, UNSW Sydney, NSW, Australia.
Bioinformatics. 2019 Dec 15;35(24):5372-5373. doi: 10.1093/bioinformatics/btz586.
The management of raw nanopore sequencing data poses a challenge that must be overcome to facilitate the creation of new bioinformatics algorithms predicated on signal analysis. SquiggleKit is a toolkit for manipulating and interrogating nanopore data that simplifies file handling, data extraction, visualization and signal processing.
SquiggleKit is cross platform and freely available from GitHub at (https://github.com/Psy-Fer/SquiggleKit). Detailed documentation can be found at (https://psy-fer.github.io/SquiggleKitDocs/). All tools have been designed to operate in python 2.7+, with minimal additional libraries.
Supplementary data are available at Bioinformatics online.
原始纳米孔测序数据的管理是一个必须克服的挑战，以便为基于信号分析的新生物信息学算法的创建提供便利。SquiggleKit 是一个用于操作和查询纳米孔数据的工具包，它简化了文件处理、数据提取、可视化和信号处理。
SquiggleKit 是跨平台的，可从 GitHub 上免费获得（https://github.com/Psy-Fer/SquiggleKit）。详细文档可在（https://psy-fer.github.io/SquiggleKitDocs/）找到。所有工具都被设计为在 python 2.7+ 中运行，只需最少的额外库。
补充数据可在生物信息学在线获得。
