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通过结合长读长和短读长来优化排行榜宏基因组学的测序方案。

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

机构信息

Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA.

Center for Algorithmic Biotechnology, Institute for Translational Biomedicine, St. Petersburg State University, St. Petersburg, Russia.

出版信息

Genome Biol. 2019 Oct 31;20(1):226. doi: 10.1186/s13059-019-1834-9.

Abstract

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

摘要

随着宏基因组研究的样本数量不断增加,人类肠道等群落可能会从大量样本中丰富微生物的组装中受益更多,而不是从少数样本中进行详尽的组装。我们将这种方法称为排行榜宏基因组测序。为了探索实际样本中排行榜宏基因组学的方案优化,我们引入了一个使用合成长读技术生成的内部参考物进行文库制备和测序的基准测试,使我们能够针对源自样本本身的黄金标准参考基因组来评估高通量文库制备方法。我们引入了一种低成本的高通量文库制备和测序方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8d7/6822431/7df710e7651a/13059_2019_1834_Fig1_HTML.jpg

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