通过使用 skani 进行稀疏链接实现快速稳健的宏基因组序列比较。
Fast and robust metagenomic sequence comparison through sparse chaining with skani.
机构信息
Department of Mathematics, University of Toronto, Toronto, Ontario, Canada.
Computer and Mathematical Sciences, University of Toronto at Scarborough, Toronto, Ontario, Canada.
出版信息
Nat Methods. 2023 Nov;20(11):1661-1665. doi: 10.1038/s41592-023-02018-3. Epub 2023 Sep 21.
Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani ( https://github.com/bluenote-1577/skani ), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic datasets.
用于宏基因组组装基因组（MAG）的序列比较工具在处理大容量或低质量数据时存在困难。我们提出了 skani（https://github.com/bluenote-1577/skani），这是一种通过稀疏近似比对来确定平均核苷酸同一性（ANI）的方法。对于碎片化、不完整的 MAG，skani 在准确性和速度（快 20 倍以上）方面优于 FastANI。skani 可以在几秒钟内使用 6GB 内存查询超过 65,000 个原核基因组，以秒为单位。skani 为广泛、嘈杂的宏基因组数据集解锁了更高分辨率的见解。
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