前情提要NBT：QIIME 2可重复、交互式的微生物组分析平台1简介和安装Introduction&Install2插件工作流程概述Workflow3老司机上路指南Experienced4人体各部位微生物组分析Moving Pictures
，Genome Biology：人体各部位微生物组时间序列分析5粪菌移植分析练习FMT，Microbiome：粪菌移植改善自闭症6沙漠土壤分析Atacama soil，mSystems：干旱对土壤微生物组的影响
7帕金森小鼠教程Parkinson’s Mouse，Cell：肠道菌群促进帕金森发生ParkinsonDisease8差异丰度分析gneiss9数据导入Importing data10数据导出Exporting data
11元数据Metadata12数据筛选Filtering data13训练特征分类器Training feature classifiers14数据评估和质控Evaluating and controlling
15样品分类和回归q2-sample-classifier16纵向和成对样本比较q2-longitudinal17鉴定和过滤嵌合体序列q2-vsearch18序列双端合并read-joining19使用q2-vsearch聚类OTUs
QIIME 2中的实用程序Utilities in QIIME 2https://docs.qiime2.org/2020.2/tutorials/utilities/以下是QIIME 2中提供的许多非基于插件的实用程序。
以下文档试图演示其中的许多功能本文档按接口interface划分，并尝试交叉引用其他接口中可用的类似功能命令行q2cli大多数有趣的实用程序都可以在q2cli的tools子命令中找到：qiime tools --help
显示如下结果:Usage: qiime tools [OPTIONS] COMMAND [ARGS]... 用于QIIME 2文件的工具Tools for working with QIIME 2 files.。
Options: --help 显示帮助并退出Show this message and exit.Commands: citations 显示引文Print citations for a QIIME 2 result.
export 导出数据Export data from a QIIME 2 Artifact or a Visualization extract 解压对象Extract a QIIME 2 Artifact or Visualization archive.
import 导入数据Import data into a new QIIME 2 Artifact. inspect-metadata 检查元数据列Inspect columns available in metadata.
peek 预览Take a peek at a QIIME 2 Artifact or Visualization. validate 验证Validate data in a QIIME 2 Artifact.
view 查看View a QIIME 2 Visualization.让我们动手处理一些数据，以便我们可以进一步了解此功能！首先，我们将查看PD Mice教程中的分类条形图：
mkdir -p utilites && cd utiliteswget -c "https://data.qiime2.org/2020.2/tutorials/utilities/taxa-barplot.qzv" \
-O "taxa-barplot.qzv"检索引文 Retrieving Citations现在我们有了一些结果，让我们更多地了解与创建此可视化相关的引文首先，我们可以检查qiime tools citations。
命令的帮助文本：qiime tools citations --help输出：Usage: qiime tools citations [OPTIONS] ARTIFACT/VISUALIZATION Print citations as a BibTex file (.bib) for a QIIME 2 result.
Options: --help Show this message and exit.输出可视化：taxa-barplot.qzv: 查看 | 下载现在我们知道如何使用该命令，我们将运行以下命令：
qiime tools citations taxa-barplot.qzv输出结果如下：@article{framework|qiime2:2019.10.0|0, author = {Bolyen, Evan and Rideout, Jai Ram and Dillon, Matthew R. and Bokulich, Nicholas A. and Abnet, Christian C. and Al-Ghalith, Gabriel A. and Alexander, Harriet and Alm, Eric J. and Arumugam, Manimozhiyan and Asnicar, Francesco and Bai, Yang and Bisanz, Jordan E. and Bittinger, Kyle and Brejnrod, Asker and Brislawn, Colin J. and Brown, C. Titus and Callahan, Benjamin J. and Caraballo-Rodríguez, Andrés Mauricio and Chase, John and Cope, Emily K. and Da Silva, Ricardo and Diener, Christian and Dorrestein, Pieter C. and Douglas, Gavin M. and Durall, Daniel M. and Duvallet, Claire and Edwardson, Christian F. and Ernst, Madeleine and Estaki, Mehrbod and Fouquier, Jennifer and Gauglitz, Julia M. and Gibbons, Sean M. and Gibson, Deanna L. and Gonzalez, Antonio and Gorlick, Kestrel and Guo, Jiarong and Hillmann, Benjamin and Holmes, Susan and Holste, Hannes and Huttenhower, Curtis and Huttley, Gavin A. and Janssen, Stefan and Jarmusch, Alan K. and Jiang, Lingjing and Kaehler, Benjamin D. and Kang, Kyo Bin and Keefe, Christopher R. and Keim, Paul and Kelley, Scott T. and Knights, Dan and Koester, Irina and Kosciolek, Tomasz and Kreps, Jorden and Langille, Morgan G. I. and Lee, Joslynn and Ley, Ruth and Liu, Yong-Xin and Loftfield, Erikka and Lozupone, Catherine and Maher, Massoud and Marotz, Clarisse and Martin, Bryan D. and McDonald, Daniel and McIver, Lauren J. and Melnik, Alexey V. and Metcalf, Jessica L. and Morgan, Sydney C. and Morton, Jamie T. and Naimey, Ahmad Turan and Navas-Molina, Jose A. and Nothias, Louis Felix and Orchanian, Stephanie B. and Pearson, Talima and Peoples, Samuel L. and Petras, Daniel and Preuss, Mary Lai and Pruesse, Elmar and Rasmussen, Lasse Buur and Rivers, Adam and Robeson, Michael S. and Rosenthal, Patrick and Segata, Nicola and Shaffer, Michael and Shiffer, Arron and Sinha, Rashmi and Song, Se Jin and Spear, John R. and Swafford, Austin D. and Thompson, Luke R. and Torres, Pedro J. and Trinh, Pauline and Tripathi, Anupriya and Turnbaugh, Peter J. and Ul-Hasan, Sabah and van der Hooft, Justin J. J. and Vargas, Fernando and Vázquez-Baeza, Yoshiki and Vogtmann, Emily and von Hippel, Max and Walters, William and Wan, Yunhu and Wang, Mingxun and Warren, Jonathan and Weber, Kyle C. and Williamson, Charles H. D. and Willis, Amy D. and Xu, Zhenjiang Zech and Zaneveld, Jesse R. and Zhang, Yilong and Zhu, Qiyun and Knight, Rob and Caporaso, J. Gregory},
doi = {10.1038/s41587-019-0209-9}, issn = {1546-1696}, journal = {Nature Biotechnology}, number = {8},
pages = {852-857}, title = {Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2},
url = {https://doi.org/10.1038/s41587-019-0209-9}, volume = {37}, year = {2019}}@article{view|types:2019.10.0|BIOMV210DirFmt|0,
author = {McDonald, Daniel and Clemente, Jose C and Kuczynski, Justin and Rideout, Jai Ram and Stombaugh, Jesse and Wendel, Doug and Wilke, Andreas and Huse, Susan and Hufnagle, John and Meyer, Folker and Knight, Rob and Caporaso, J Gregory},
doi = {10.1186/2047-217X-1-7}, journal = {GigaScience}, number = {1}, pages = {7}, publisher = {BioMed Central},
title = {The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome},
volume = {1}, year = {2012}}@inproceedings{view|types:2019.10.0|pandas.core.frame:DataFrame|0, author = { Wes McKinney },
booktitle = { Proceedings of the 9th Python in Science Conference }, editor = { Stéfan van der Walt and Jarrod Millman },
pages = { 51 -- 56 }, title = { Data Structures for Statistical Computing in Python }, year = { 2010 }
}@inproceedings{view|types:2019.10.0|pandas.core.series:Series|0, author = { Wes McKinney }, booktitle = { Proceedings of the 9th Python in Science Conference },
editor = { Stéfan van der Walt and Jarrod Millman }, pages = { 51 -- 56 }, title = { Data Structures for Statistical Computing in Python },
year = { 2010 }}@article{view|types:2019.10.0|biom.table:Table|0, author = {McDonald, Daniel and Clemente, Jose C and Kuczynski, Justin and Rideout, Jai Ram and Stombaugh, Jesse and Wendel, Doug and Wilke, Andreas and Huse, Susan and Hufnagle, John and Meyer, Folker and Knight, Rob and Caporaso, J Gregory},
doi = {10.1186/2047-217X-1-7}, journal = {GigaScience}, number = {1}, pages = {7}, publisher = {BioMed Central},
title = {The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome},
volume = {1}, year = {2012}}@article{framework|qiime2:2019.4.0|0, author = {Bolyen, Evan and Rideout, Jai Ram and Dillon, Matthew R and Bokulich, Nicholas A and Abnet, Christian and Al-Ghalith, Gabriel A and Alexander, Harriet and Alm, Eric J and Arumugam, Manimozhiyan and Asnicar, Francesco and Bai, Yang and Bisanz, Jordan E and Bittinger, Kyle and Brejnrod, Asker and Brislawn, Colin J and Brown, C Titus and Callahan, Benjamin J and Caraballo-Rodríguez, Andrés Mauricio and Chase, John and Cope, Emily and Da Silva, Ricardo and Dorrestein, Pieter C and Douglas, Gavin M and Durall, Daniel M and Duvallet, Claire and Edwardson, Christian F and Ernst, Madeleine and Estaki, Mehrbod and Fouquier, Jennifer and Gauglitz, Julia M and Gibson, Deanna L and Gonzalez, Antonio and Gorlick, Kestrel and Guo, Jiarong and Hillmann, Benjamin and Holmes, Susan and Holste, Hannes and Huttenhower, Curtis and Huttley, Gavin and Janssen, Stefan and Jarmusch, Alan K and Jiang, Lingjing and Kaehler, Benjamin and Kang, Kyo Bin and Keefe, Christopher R and Keim, Paul and Kelley, Scott T and Knights, Dan and Koester, Irina and Kosciolek, Tomasz and Kreps, Jorden and Langille, Morgan GI and Lee, Joslynn and Ley, Ruth and Liu, Yong-Xin and Loftfield, Erikka and Lozupone, Catherine and Maher, Massoud and Marotz, Clarisse and Martin, Bryan and McDonald, Daniel and McIver, Lauren J and Melnik, Alexey V and Metcalf, Jessica L and Morgan, Sydney C and Morton, Jamie and Naimey, Ahmad Turan and Navas-Molina, Jose A and Nothias, Louis Felix and Orchanian, Stephanie B and Pearson, Talima and Peoples, Samuel L and Petras, Daniel and Preuss, Mary Lai and Pruesse, Elmar and Rasmussen, Lasse Buur and Rivers, Adam and Robeson, II, Michael S and Rosenthal, Patrick and Segata, Nicola and Shaffer, Michael and Shiffer, Arron and Sinha, Rashmi and Song, Se Jin and Spear, John R and Swafford, Austin D and Thompson, Luke R and Torres, Pedro J and Trinh, Pauline and Tripathi, Anupriya and Turnbaugh, Peter J and Ul-Hasan, Sabah and van der Hooft, Justin JJ and Vargas, Fernando and Vázquez-Baeza, Yoshiki and Vogtmann, Emily and von Hippel, Max and Walters, William and Wan, Yunhu and Wang, Mingxun and Warren, Jonathan and Weber, Kyle C and Williamson, Chase HD and Willis, Amy D and Xu, Zhenjiang Zech and Zaneveld, Jesse R and Zhang, Yilong and Knight, Rob and Caporaso, J Gregory},
doi = {10.7287/peerj.preprints.27295v1}, issn = {2167-9843}, journal = {PeerJ Preprints}, month = {oct},
pages = {e27295v1}, title = {QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science},
url = {https://doi.org/10.7287/peerj.preprints.27295v1}, volume = {6}, year = {2018}}@article{action|feature-classifier:2019.4.0|method:fit_classifier_naive_bayes|0,
author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard},
journal = {Journal of machine learning research}, number = {Oct}, pages = {2825--2830}, title = {Scikit-learn: Machine learning in Python},
volume = {12}, year = {2011}}@inproceedings{view|types:2019.4.1|pandas.core.series:Series|0, author = { Wes McKinney },
booktitle = { Proceedings of the 9th Python in Science Conference }, editor = { Stéfan van der Walt and Jarrod Millman },
pages = { 51 -- 56 }, title = { Data Structures for Statistical Computing in Python }, year = { 2010 }
}@article{plugin|feature-classifier:2019.4.0|0, author = {Bokulich, Nicholas A. and Kaehler, Benjamin D. and Rideout, Jai Ram and Dillon, Matthew and Bolyen, Evan and Knight, Rob and Huttley, Gavin A. and Caporaso, J. Gregory},
doi = {10.1186/s40168-018-0470-z}, journal = {Microbiome}, number = {1}, pages = {90}, title = {Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2s q2-feature-classifier plugin},
url = {https://doi.org/10.1186/s40168-018-0470-z}, volume = {6}, year = {2018}}@article{plugin|dada2:2019.10.0|0,
author = {Callahan, Benjamin J and McMurdie, Paul J and Rosen, Michael J and Han, Andrew W and Johnson, Amy Jo A and Holmes, Susan P},
doi = {10.1038/nmeth.3869}, journal = {Nature methods}, number = {7}, pages = {581}, publisher = {Nature Publishing Group},
title = {DADA2: high-resolution sample inference from Illumina amplicon data}, volume = {13}, year = {2016}
}@article{action|feature-classifier:2019.10.0|method:classify_sklearn|0, author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard},
journal = {Journal of machine learning research}, number = {Oct}, pages = {2825--2830}, title = {Scikit-learn: Machine learning in Python},
volume = {12}, year = {2011}}@article{plugin|feature-classifier:2019.10.0|0, author = {Bokulich, Nicholas A. and Kaehler, Benjamin D. and Rideout, Jai Ram and Dillon, Matthew and Bolyen, Evan and Knight, Rob and Huttley, Gavin A. and Caporaso, J. Gregory},
doi = {10.1186/s40168-018-0470-z}, journal = {Microbiome}, number = {1}, pages = {90}, title = {Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2s q2-feature-classifier plugin},
url = {https://doi.org/10.1186/s40168-018-0470-z}, volume = {6}, year = {2018}}如您所见，上面以BibTeX格式显示了此特定可视化的引文。
我们还可以看到特定插件的引用：qiime vsearch --citations显示如下：% use `qiime tools citations` on a QIIME 2 result for complete list
@article{key0, author = {Rognes, Torbjørn and Flouri, Tomáš and Nichols, Ben and Quince, Christopher and Mahé, Frédéric},
doi = {10.7717/peerj.2584}, journal = {PeerJ}, pages = {e2584}, publisher = {PeerJ Inc.}, title = {VSEARCH: a versatile open source tool for metagenomics},
volume = {4}, year = {2016}}以及针对插件的特定操作：qiime vsearch cluster-features-open-reference --citations显示如下：
% use `qiime tools citations` on a QIIME 2 result for complete list@article{key0, author = {Rideout, Jai Ram and He, Yan and Navas-Molina, Jose A. and Walters, William A. and Ursell, Luke K. and Gibbons, Sean M. and Chase, John and McDonald, Daniel and Gonzalez, Antonio and Robbins-Pianka, Adam and Clemente, Jose C. and Gilbert, Jack A. and Huse, Susan M. and Zhou, Hong-Wei and Knight, Rob and Caporaso, J. Gregory},
doi = {10.7717/peerj.545}, journal = {PeerJ}, pages = {e545}, publisher = {PeerJ Inc.}, title = {Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences},
volume = {2}, year = {2014}}查看可视化 Viewing Visualizations如果我们要查看分类单元图怎么办？一种选择是在https://view.qiime2.org上加载可视化文件。
另一种选择是使用qiime工具视图来完成工作注意：只能在https://view.qiime2.org上查看出处qiime tools view taxa-barplot.qzv此步需要图形界面支持如Linux/Mac系统的桌面下运行。
Widnows可使用Linux的远程桌面，详见（Windows10远程桌面Ubuntu），或Termial配置支持X11转发(如XShell+Xmanager，或Putty+xming，不推荐，反应极慢)。
这将打开一个浏览器窗口，其中包含您的可视化文件完成后，您可以关闭浏览器窗口并按键盘上的ctrl-c终止命令偷看结果 Peeking at Results通常，我们需要验证对象的类型和uuid我们可以使用。
qiime tools peek命令来查看这些对象的简短摘要报告首先，让我们看一些数据：请选择最适合您的环境的下载选项：wget \ -O "faith-pd-vector.qza" \ "https://data.qiime2.org/2020.2/tutorials/utilities/faith-pd-vector.qza"。
现在我们有了数据，我们可以了解有关该文件的更多信息：qiime tools peek faith-pd-vector.qza显示结果如下：UUID: d5186dce-438d-44bb-903c-cb51a7ad4abe
Type: SampleData[AlphaDiversity] % Properties(phylogenetic)Data format: AlphaDiversityDirectoryFormat
输出对象：faith-pd-vector.qza: 查看 | 下载在这里，我们可以看到对象的类型为SampleData [AlphaDiversity]％Properties（phylogenetic）
，以及对象的UUID和格式验证结果 Validating Results我们还可以通过运行qiime tools validate来验证文件的完整性qiime tools validate faith-pd-vector.qza。
显示如下结果Result faith-pd-vector.qza appears to be valid at level=max.如果文件有问题，此命令通常会在在合理范围内很好地报告问题所在检查元数据 Inspecting Metadata。
在元数据教程中，我们了解了metadata tabulate命令及其创建的可视化效果通常，我们不太关心元数据的值，而只是关心它的结构：多少列？他们的名字是什么？他们是什么类型？文件中有多少行（或ID）？。
我们可以通过首先下载一些示例元数据来演示这一点：wget -c "https://data.qiime2.org/2020.2/tutorials/pd-mice/sample_metadata.tsv" \
-O "sample-metadata.tsv"然后运行qiime tools inspect-metadata命令：qiime tools inspect-metadata sample-metadata.tsv
显示如下结果： COLUMN NAME TYPE======================== =========== barcode categorical
mouse_id categorical genotype categorical cage_id categorical
donor categorical donor_status categorical days_post_transplant numeric
enotype_and_donor_status categorical======================== =========== IDS: 48
COLUMNS: 8问题：sample-metadata.tsv中有多少个元数据列？多少个ID？确定存在多少分类列该工具对于了解可作为元数据查看的文件的元数据列名称很有帮助。
详者注：我们知道行列数量(48行/IDS代表48个样品，8列/COLUMNS代表有8种样本属性)，以及他们分别是属于分类型catagorical或是数值型numericwget -c "https://data.qiime2.org/2020.2/tutorials/utilities/jaccard-pcoa.qza" \。
-O "jaccard-pcoa.qza"我们刚刚下载的文件是Jaccard PCoA（来自PD Mice教程），可以代替“典型” TSV格式的元数据文件使用我们可能需要了解我们希望运行的命令的列名，使用inspect-metadata，我们可以了解所有信息：。
qiime tools inspect-metadata jaccard-pcoa.qza结果如下：COLUMN NAME TYPE=========== ======= Axis 1 numeric
Axis 2 numeric Axis . numeric Axis 47 numeric=========== ======= IDS: 47 COLUMNS: 47
输出对象：jaccard-pcoa.qza: 查看 | 下载问题：有多少个ID？多少列？是否有分类型的列？为什么？详者注：共有47个IDS，47列，无分类型列因为PCoA的结果为坐标值，为数值型对象接口 Artifact API。
即将推出，请继续关注！Referencehttps://docs.qiime2.org/2020.2Evan Bolyen, Jai Ram Rideout, Matthew R. Dillon, Nicholas A. Bokulich
, Christian C. Abnet, Gabriel A. Al-Ghalith, Harriet Alexander, Eric J. Alm, Manimozhiyan Arumugam, Francesco Asnicar, Yang Bai, Jordan E. Bisanz, Kyle Bittinger, Asker Brejnrod, Colin J. Brislawn, C. Titus Brown, Benjamin J. Callahan, Andrés Mauricio Caraballo-Rodríguez, John Chase, Emily K. Cope, Ricardo Da Silva, Christian Diener, Pieter C. Dorrestein, Gavin M. Douglas, Daniel M. Durall, Claire Duvallet, Christian F. Edwardson, Madeleine Ernst, Mehrbod Estaki, Jennifer Fouquier, Julia M. Gauglitz, Sean M. Gibbons, Deanna L. Gibson, Antonio Gonzalez, Kestrel Gorlick, Jiarong Guo, Benjamin Hillmann, Susan Holmes, Hannes Holste, Curtis Huttenhower, Gavin A. Huttley, Stefan Janssen, Alan K. Jarmusch, Lingjing Jiang, Benjamin D. Kaehler, Kyo Bin Kang, Christopher R. Keefe, Paul Keim, Scott T. Kelley, Dan Knights, Irina Koester, Tomasz Kosciolek, Jorden Kreps, Morgan G. I. Langille, Joslynn Lee, Ruth Ley,
Yong-Xin Liu, Erikka Loftfield, Catherine Lozupone, Massoud Maher, Clarisse Marotz, Bryan D. Martin, Daniel McDonald, Lauren J. McIver, Alexey V. Melnik, Jessica L. Metcalf, Sydney C. Morgan, Jamie T. Morton, Ahmad Turan Naimey, Jose A. Navas-Molina, Louis Felix Nothias, Stephanie B. Orchanian, Talima Pearson, Samuel L. Peoples, Daniel Petras, Mary Lai Preuss, Elmar Pruesse, Lasse Buur Rasmussen, Adam Rivers, Michael S. Robeson, Patrick Rosenthal, Nicola Segata, Michael Shaffer, Arron Shiffer, Rashmi Sinha, Se Jin Song, John R. Spear, Austin D. Swafford, Luke R. Thompson, Pedro J. Torres, Pauline Trinh, Anupriya Tripathi, Peter J. Turnbaugh, Sabah Ul-Hasan, Justin J. J. van der Hooft, Fernando Vargas, Yoshiki Vázquez-Baeza, Emily Vogtmann, Max von Hippel, William Walters, Yunhu Wan, Mingxun Wang, Jonathan Warren, Kyle C. Weber, Charles H. D. Williamson, Amy D. Willis, Zhenjiang Zech Xu, Jesse R. Zaneveld, Yilong Zhang, Qiyun Zhu, Rob Knight & J. Gregory Caporaso#. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
Nature Biotechnology. 2019, 37: 852-857. doi:10.1038/s41587-019-0209-9译者简介刘永鑫，博士2008年毕业于东北农大微生物学，2014年于中科院遗传发育所获生物信息学博士，2016年遗传学博士后出站留所工作，任宏基因组学实验室工程师。
目前主要研究方向为微生物组数据分析、分析方法开发与优化和科学传播，QIIME 2项目参与人目前在Science、Nature Biotechnology、Cell Host & Microbe、Current Opinion in Microbiology
等杂志发表论文20余篇2017年7月创办“宏基因组”公众号，目前分享宏基因组、扩增子原创文章500余篇，代表博文有《扩增子图表解读、分析流程和统计绘图三部曲(21篇)》、《Nature综述：手把手教你分析菌群数据(1.8万字)》。
、《QIIME2中文教程(22篇)》等，关注人数8万+，累计阅读1300万+猜你喜欢10000+：菌群分析宝宝与猫狗梅毒狂想曲提DNA发NatureCell专刊肠道指挥大脑系列教程：微生物组入门Biostar。
微生物组宏基因组专业技能：学术图表高分文章生信宝典不可或缺的人一文读懂：宏基因组寄生虫益处进化树必备技能：提问搜索Endnote文献阅读 热心肠SemanticScholarGeenmedical扩增子分析：
图表解读分析流程统计绘图16S功能预测PICRUStFAPROTAXBugbaseTax4Fun在线工具：16S预测培养基生信绘图科研经验：云笔记云协作公众号编程模板: ShellRPerl生物科普:
肠道细菌人体上的生命生命大跃进细胞暗战人体奥秘写在后面为鼓励读者交流、快速解决科研困难，我们建立了“宏基因组”专业讨论群，目前己有国内外5000+ 一线科研人员加入参与讨论，获得专业解答，欢迎分享此文至朋友圈，并扫码加主编好友带你入群，务必备注“姓名-单位-研究方向-职称/年级”。
PI请明示身份，另有海内外微生物相关PI群供大佬合作交流。技术问题寻求帮助，首先阅读《如何优雅的提问》学习解决问题思路，仍未解决群内讨论，问题不私聊，帮助同行。
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