Microfluidic Enrichment and Computational Analysis of Rare Sequences from Mixed Genomic Samples for Metagenomic Mining.

Microfluidic Enrichment and Computational Analysis of Rare Sequences from Mixed Genomic Samples for Metagenomic Mining.
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DOI:
10.1089/crispr.2022.0054
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发表时间:
2022-10
期刊:
The CRISPR journal
影响因子:
--
通讯作者:
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其他
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许多强大的分子生物学工具都起源于自然系统,包括限制性修饰酶和CRISPR效应子,Cas9,Cas12和Cas13。对这些系统的兴趣增加,导致了对基因组和宏基因组数据的挖掘,以识别这些蛋白质的新直系同源物、新型CRISPR系统和具有新机制的未表征的自然系统。为了加速宏基因组挖掘,我们开发了一种高通量、低成本的基于液滴微流体的方法,用于在混合起始群体中富集稀有序列。使用计算管道,我们然后在丰富的数据中搜索CRISPR-Cas系统的存在,识别出以前未知的CRISPR-Cas系统。我们的方法使研究人员能够有效地挖掘宏基因组样本中的感兴趣序列,大大加快了对自然宝藏的搜索。
Many powerful molecular biology tools have their origins in natural systems, including restriction modification enzymes and the CRISPR effectors, Cas9, Cas12, and Cas13. Heightened interest in these systems has led to mining of genomic and metagenomic data to identify new orthologs of these proteins, new types of CRISPR systems, and uncharacterized natural systems with novel mechanisms. To accelerate metagenomic mining, we developed a high-throughput, low-cost droplet microfluidic-based method for enrichment of rare sequences in a mixed starting population. Using a computational pipeline, we then searched in the enriched data for the presence of CRISPR-Cas systems, identifying a previously unknown CRISPR-Cas system. Our approach enables researchers to efficiently mine metagenomic samples for sequences of interest, greatly accelerating the search for nature's treasures.
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