SNIKT：长读测序数据中序列无关接头的识别与去除。
SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data.
机构信息
Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Institute for Research on Innovation and Science, Institute for Social Research, University of Michigan, Ann Arbor, MI 48109, USA.
出版信息
Bioinformatics. 2022 Aug 2;38(15):3830-3832. doi: 10.1093/bioinformatics/btac389.
SUMMARY
Here, we introduce SNIKT, a command-line tool for sequence-independent visual confirmation and input-assisted removal of adapter contamination in whole-genome shotgun or metagenomic shotgun long-read sequencing DNA or RNA data.
AVAILABILITY AND IMPLEMENTATION
SNIKT is implemented in R and is compatible with Unix-like platforms. The source code, along with documentation, is freely available under an MIT license at https://github.com/piyuranjan/SNIKT.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
摘要
在这里，我们介绍了 SNIKT，这是一个用于全基因组鸟枪法或宏基因组鸟枪法长读测序 DNA 或 RNA 数据的无序列依赖性视觉确认和输入辅助去除接头污染的命令行工具。
可用性和实现
SNIKT 是用 R 编写的，与类 Unix 平台兼容。源代码以及文档都可以在 MIT 许可证下免费获得，网址为 https://github.com/piyuranjan/SNIKT。
补充信息
补充数据可在 Bioinformatics 在线获取。
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