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本文引用的文献

1
The gut microbiota of nonalcoholic fatty liver disease: current methods and their interpretation.非酒精性脂肪性肝病的肠道微生物群:当前方法及其解读
Hepatol Int. 2015 Jul;9(3):406-15. doi: 10.1007/s12072-015-9640-2. Epub 2015 Jun 12.
2
New perspectives on microbial community distortion after whole-genome amplification.全基因组扩增后微生物群落畸变的新视角。
PLoS One. 2015 May 26;10(5):e0124158. doi: 10.1371/journal.pone.0124158. eCollection 2015.
3
Sequencing and beyond: integrating molecular 'omics' for microbial community profiling.测序及其他:整合分子“组学”技术进行微生物群落分析
Nat Rev Microbiol. 2015 Jun;13(6):360-72. doi: 10.1038/nrmicro3451. Epub 2015 Apr 27.
4
MUSiCC: a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.MUSiCC:一种基于标记基因的宏基因组标准化和微生物组中基因丰度准确分析的框架。
Genome Biol. 2015 Mar 25;16(1):53. doi: 10.1186/s13059-015-0610-8.
5
The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies.宏基因组学的真相:量化和抵消16S rRNA研究中的偏差
BMC Microbiol. 2015 Mar 21;15:66. doi: 10.1186/s12866-015-0351-6.
6
Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome.平均基因组大小估计改善了比较宏基因组学,并揭示了人类微生物组的功能生态学。
Genome Biol. 2015 Mar 25;16(1):51. doi: 10.1186/s13059-015-0611-7.
7
Evaluating variation in human gut microbiota profiles due to DNA extraction method and inter-subject differences.评估由于 DNA 提取方法和个体间差异导致的人类肠道微生物组特征的变化。
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8
Limits to robustness and reproducibility in the demarcation of operational taxonomic units.在操作分类单元的划分中,稳健性和可再现性的限制。
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Relating the metatranscriptome and metagenome of the human gut.人类肠道的宏转录组和宏基因组关联分析。
Proc Natl Acad Sci U S A. 2014 Jun 3;111(22):E2329-38. doi: 10.1073/pnas.1319284111. Epub 2014 May 19.
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Variations in 16S rRNA-based microbiome profiling between pyrosequencing runs and between pyrosequencing facilities.基于16S rRNA的微生物群落分析在焦磷酸测序运行之间以及焦磷酸测序设施之间的差异。
J Microbiol. 2014 May;52(5):355-65. doi: 10.1007/s12275-014-3443-3. Epub 2014 Apr 11.

文库制备方法会影响人类微生物组研究中的基因组和功能预测。

Library preparation methodology can influence genomic and functional predictions in human microbiome research.

作者信息

Jones Marcus B, Highlander Sarah K, Anderson Ericka L, Li Weizhong, Dayrit Mark, Klitgord Niels, Fabani Martin M, Seguritan Victor, Green Jessica, Pride David T, Yooseph Shibu, Biggs William, Nelson Karen E, Venter J Craig

机构信息

Human Longevity, Inc., San Diego, CA 92121; Genomic Medicine, J. Craig Venter Institute, La Jolla, CA 92037;

Genomic Medicine, J. Craig Venter Institute, La Jolla, CA 92037;

出版信息

Proc Natl Acad Sci U S A. 2015 Nov 10;112(45):14024-9. doi: 10.1073/pnas.1519288112. Epub 2015 Oct 28.

DOI:10.1073/pnas.1519288112
PMID:26512100
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4653211/
Abstract

Observations from human microbiome studies are often conflicting or inconclusive. Many factors likely contribute to these issues including small cohort sizes, sample collection, and handling and processing differences. The field of microbiome research is moving from 16S rDNA gene sequencing to a more comprehensive genomic and functional representation through whole-genome sequencing (WGS) of complete communities. Here we performed quantitative and qualitative analyses comparing WGS metagenomic data from human stool specimens using the Illumina Nextera XT and Illumina TruSeq DNA PCR-free kits, and the KAPA Biosystems Hyper Prep PCR and PCR-free systems. Significant differences in taxonomy are observed among the four different next-generation sequencing library preparations using a DNA mock community and a cell control of known concentration. We also revealed biases in error profiles, duplication rates, and loss of reads representing organisms that have a high %G+C content that can significantly impact results. As with all methods, the use of benchmarking controls has revealed critical differences among methods that impact sequencing results and later would impact study interpretation. We recommend that the community adopt PCR-free-based approaches to reduce PCR bias that affects calculations of abundance and to improve assemblies for accurate taxonomic assignment. Furthermore, the inclusion of a known-input cell spike-in control provides accurate quantitation of organisms in clinical samples.

摘要

人类微生物组研究的观察结果往往相互矛盾或无定论。许多因素可能导致这些问题,包括队列规模小、样本采集以及处理和加工差异。微生物组研究领域正从16S rDNA基因测序转向通过对完整群落进行全基因组测序(WGS)来实现更全面的基因组和功能表征。在这里,我们使用Illumina Nextera XT和Illumina TruSeq DNA PCR-free试剂盒以及KAPA Biosystems Hyper Prep PCR和PCR-free系统,对来自人类粪便标本的WGS宏基因组数据进行了定量和定性分析。使用DNA模拟群落和已知浓度的细胞对照,在四种不同的下一代测序文库制备方法中观察到分类学上的显著差异。我们还揭示了错误谱、重复率以及代表高%G+C含量生物体的读数丢失方面的偏差,这些偏差会显著影响结果。与所有方法一样,使用基准对照揭示了影响测序结果且随后会影响研究解释的方法之间的关键差异。我们建议业界采用基于免PCR的方法,以减少影响丰度计算的PCR偏差,并改进组装以进行准确的分类学归属。此外,加入已知输入的细胞加标对照可对临床样本中的生物体进行准确定量。