Sigmoni: classification of nanopore signal with a compressed pangenome index.
Sigmoni: classification of nanopore signal with a compressed pangenome index.
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Sigmoni：使用压缩的泛基因组索引对纳米孔信号进行分类。
DOI：
10.1101/2023.08.15.553308
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Langmead,Ben
中科院分区：
文献类型：
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作者：
Shivakumar,VikramS;Ahmed,OmarY;Kovaka,Sam;Zakeri,Mohsen;Langmead,Ben
摘要纳米孔测序的改进需要有效的分类方法，包括预过滤和自适应采样算法，以丰富感兴趣的读数。基于信号的方法规避了碱基识别的计算瓶颈。但过去基于信号的分类方法无法有效地扩展到大型重复参考（例如泛基因组），从而限制了它们对部分参考或单个基因组的实用性。我们介绍 Sigmoni：一种基于 ther-index 的快速多类分类方法，可扩展到数百 Gbps 的参考。西格莫尼将纳米孔信号量化为皮安范围的离散字母表。它使用匹配统计数据执行快速、近似匹配，根据皮安匹配统计数据和共线性统计数据的分布对读取进行分类，所有这些都在线性查询时间内完成，无需种子链扩展。 Sigmoni 比以前在宿主耗竭实验中进行自适应采样的方法快 10-100 倍，并且精度更高，并且可以针对大型微生物或人类全基因组查询读数。 Sigmoni 是第一个基于信号的工具，可扩展到完整的人类基因组和泛基因组，同时保持足够快的速度以适应自适应采样应用。可用性和实现Sigmoni 用 Python 实现，可在 https://github.com/vshiv18/sigmoni 上开源。
SummaryImprovements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on ther-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications.Availability and implementationSigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.