利用 Maast 和 GT-Pro 快速准确地进行种特异性 k--mer 宏基因组分型。
Identifying species-specific k-mers for fast and accurate metagenotyping with Maast and GT-Pro.
机构信息
Chan Zuckerberg Biohub, San Francisco, CA, USA; Gladstone Institutes, Data Science and Biotechnology, San Francisco, CA, USA.
Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA; Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, USA.
出版信息
STAR Protoc. 2023 Mar 17;4(1):101964. doi: 10.1016/j.xpro.2022.101964. Epub 2023 Jan 20.
Genotyping single-nucleotide polymorphisms (SNPs) in microbiomes enables strain-level quantification. In this protocol, we describe a computational pipeline that performs fast and accurate SNP genotyping using metagenomic data. We first demonstrate how to use Maast to catalog SNPs from microbial genomes. Then we use GT-Pro to extract unique SNP-covering k-mers, optimize a data structure for storing these k-mers, and finally perform metagenotyping. For proof of concept, the protocol leverages public whole-genome sequences to metagenotype a synthetic community. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022a) and Shi et al. (2022b)..
对微生物组中单核苷酸多态性（SNPs）进行基因分型可实现菌株水平的定量。在本方案中，我们描述了一个使用宏基因组数据进行快速准确 SNP 基因分型的计算流程。我们首先展示如何使用 Maast 从微生物基因组中编目 SNPs。然后，我们使用 GT-Pro 提取唯一 SNP 覆盖的 k-mer，优化存储这些 k-mer 的数据结构，最后进行宏基因分型。为了验证概念，该方案利用公共全基因组序列对人工合成群落进行宏基因分型。如需了解本方案的详细使用和执行方法，请参考 Shi 等人（2022a）和 Shi 等人（2022b）。
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