Using Networks to Study Microbes

Abstract

The goal of the course is to provide an introduction to the use of networks in microbiology. The students will receive an overview of the field of computational microbiology with special emphasis on data standardization* and data sources, and how to use graph structures to solve biological questions. Further, the students will get a hands on introduction to Python programming language and its scientific libraries. At the end of the course the students will be able to:

• Use multiple data sources for studying microbes in different biological contexts
• Build networks, visualize and analyse them using different tools
• Use Python to extract, transform and analyse different data structures

Keywords

Computational microbiology, networks, databases, Python, programming, data, pipelines, data science.

Sylabus

Time	DAY 1	DAY 2	DAY 3
8:30-9:20	Introduction and Housekeeping	Working with Data in Python I	Analysing Networks I
9:20-10:10	An Omics View on Microbes I	Working with Data in Python II
10:10-10:30	Coffee break	Coffee break	Coffee break
10:30-11:20	An Omics View on Microbes II	Visualizing Data in Python	Analysing Networks II
11:20-12:10	Open Science	Visualising Networks I
12:10-13:30	Lunch	Lunch	Lunch
13:30-14:20	Introduction to Python I	Visualising Networks II	Team Project
14:20-14:40	Coffee break	Coffee break	Coffee break
14:40-16:00	Introduction to Python II	Network Exercises	Team Project
16:00-16:50	Recap and Q & A	Recap and Q & A	Team Project Presentations and Q&A

Further Resources

References

1. ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation Patrick V Phaneuf, Dennis Gosting, Bernhard O Palsson, Adam M Feist [resource] (https://aledb.org/)

2. MiMeDB: the Human Microbial Metabolome Database David S Wishart, Eponine Oler, Harrison Peters, AnChi Guo, Sagan Girod, Scott Han, Sukanta Saha, Vicki W Lui, Marcia LeVatte, Vasuk Gautam, Rima Kaddurah-Daouk, Naama Karu resource

3. Web of microbes (WoM): a curated microbial exometabolomics database for linking chemistry and microbes Suzanne M Kosina, Annette M Greiner, Rebecca K Lau, Stefan Jenkins, Richard Baran, Benjamin P Bowen, Trent R Northen resource

4. mBodyMap: a curated database for microbes across human body and their associations with health and diseases Hanbo Jin, Guoru Hu, Chuqing Sun, Yiqian Duan, Zhenmo Zhang, Zhi Liu, Xing-Ming Zhao, Wei-Hua Chen resource

5. MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes Guocai Yao, Wenliang Zhang, Minglei Yang, Huan Yang, Jianbo Wang, Haiyue Zhang, Lai Wei, Zhi Xie, Weizhong Li resource

6. Rhea, the reaction knowledgebase in 2022 Parit Bansal, Anne Morgat, Kristian B Axelsen, Venkatesh Muthukrishnan, Elisabeth Coudert, Lucila Aimo, Nevila Hyka-Nouspikel, Elisabeth Gasteiger, Arnaud Kerhornou, Teresa Batista Neto, Monica Pozzato, Marie-Claude Blatter, Alex Ignatchenko, Nicole Redaschi, Alan Bridge resource

7. BacDive in 2022: the knowledge base for standardized bacterial and archaeal dataLorenz Christian Reimer, Joaquim Sardà Carbasse, Julia Koblitz, Christian Ebeling, Adam Podstawka, Jörg Overmann resource

8. TEMPURA: Database of Growth TEMPeratures of Usual and RAre ProkaryotesYu Sato, Kenji Okano, Hiroyuki Kimura, Kohsuke Honda resource

9. Exposome-Explorer 2.0: an update incorporating candidate dietary biomarkers and dietary associations with cancer risk Vanessa Neveu, Geneviève Nicolas, Reza M Salek, David S Wishart, Augustin Scalbert resource

10. HMDB 5.0: the Human Metabolome Database for 2022 David S Wishart, AnChi Guo, Eponine Oler, Fei Wang, Afia Anjum, Harrison Peters, Raynard Dizon, Zinat Sayeeda, Siyang Tian, Brian L Lee, Mark Berjanskii, Robert Mah, Mai Yamamoto, Juan Jovel, Claudia Torres-Calzada, Mickel Hiebert-Giesbrecht, Vicki W Lui, Dorna Varshavi, Dorsa Varshavi, Dana Allen, David Arndt, Nitya Khetarpal, Aadhavya Sivakumaran 1, Karxena Harford, Selena Sanford, Kristen Yee, Xuan Cao, Zachary Budinski, Jaanus Liigand, Lun Zhang, Jiamin Zheng, Rupasri Mandal, Naama Karu, Maija Dambrova, Helgi B Schiöth, Russell Greiner, Vasuk Gautam resource

11. MASI: microbiota—active substance interactions database Xian Zeng, Xue Yang, Jiajun Fan, Ying Tan, Lingyi Ju, Wanxiang Shen, Yali Wang, Xinghao Wang, Weiping Chen, Dianwen Ju, and Yu Zong Chen resource

12. MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes Guocai Yao, Wenliang Zhang, Minglei Yang, Huan Yang, Jianbo Wang, Haiyue Zhang, Lai Wei, Zhi Xie, Weizhong Li resource

13. iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning Kevin Rychel, Katherine Decker, Anand V Sastry, Patrick V Phaneuf, Saugat Poudel, Bernhard O Palsson resource

14. The Natural Products Atlas 2.0: a database of microbially-derived natural products Jeffrey A van Santen, Ella F Poynton, Dasha Iskakova, Emily McMann, Tyler A Alsup, Trevor N Clark, Claire H Fergusson, David P Fewer, Alison H Hughes, Caitlin A McCadden, Jonathan Parra, Sylvia Soldatou, Jeffrey D Rudolf, Elisabeth M-L Janssen, Katherine R Duncan, Roger G Linington resource

15. MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters Barbara R Terlouw, Kai Blin, Jorge C Navarro-Muñoz, ..., Dong Yang, Jingwei Yu, Mitja Zdouc, Zheng Zhong, Jérôme Collemare, Roger G Linington, Tilmann Weber, Marnix H Medema resource

16. The National Microbiome Data Collaborative: enabling microbiome science Elisha M Wood-Charlson, Anubhav, Deanna Auberry, Hannah Blanco, Mark I Borkum, Yuri E Corilo, Karen W Davenport, Shweta Deshpande, Ranjeet Devarakonda, Meghan Drake, William D Duncan, Mark C Flynn, David Hays, Bin Hu, Marcel Huntemann, Po-E Li, Mary Lipton, Chien-Chi Lo, David Millard, Kayd Miller, Paul D Piehowski, Samuel Purvine, T B K Reddy, Migun Shakya, Jagadish Chandrabose Sundaramurthi, Pajau Vangay, Yaxing Wei, Bruce E Wilson, Shane Canon, Patrick S G Chain, Kjiersten Fagnan, Stanton Martin, Lee Ann McCue, Christopher J Mungall, Nigel J Mouncey, Mary E Maxon, Emiley A Eloe-Fadrosh resource

Cheat Sheets

• Basics:
  • Getting started
  • Importing Data
  • Jupyter Notebook
• Data Science:
  • Numpy
  • Pandas
  • Scipy
  • Scikit-learn
• Visualization:
  • Matplotlib
  • Plot.ly
  • Seaborn
  • Bokeh

Basics

• learnpython.org

  • interactive python basics tutorial

• Springboard - Data Analysis with Python, SQL, and R

  • starts with - Solo Learn and Design of Computer Programs

• Scipy Lectures

  • Python introduction with a focus on scientific computing

• official tutorial

Python Installations

In this course we use Google Colab to execute notebooks. Notebooks are text files allowing the combination of Text, Code and the output of code. Colab offers an extended set of pre-installed tools. See the tutorial series.

Anaconda offers for your private computer an extended installations, including most tools you will ever need for Python.

Acknowledgements

Some of the notebooks have been inspired by the course Python Tsunami at the Center for Health Data Science (HeaDS) at the University of Copenhagen.