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Microbiome for beginners
This section is currently under construction, and we hope to collect resources (recommended reading, helpful tutorials, etc.) that are useful to those just starting out in microbiome research. This is aimed at new members of the lab group, but we think it may be useful for others, too. If there are resources that you have found helpful and we haven't included here, we'd love to hear about them. It won't be necessary to work through everything that's here in order for you to be able to process your own data, but different parts of it will probably be useful at different stages.
If you are at Dalhousie, we would encourage you to join our Dalhousie Microbiome User Group (DalMUG) - we hold a journal club every two weeks where we discuss a new microbiome paper. You can find summaries of previous papers on the site, and also join the Google group where we send out details of the next meetings and sometimes other useful things like upcoming conferences. These are informal meetings where one person will usually lead the discussion on a paper, but anyone should feel welcome to jump in with questions or comments.
These are all YouTube videos giving an overview of some aspect of microbiome sequencing or data analysis.
- Amplicon sequencing - 16S rRNA gene sequencing, Illumina YouTube webinar, What is 16S sequencing?
- Amplicon sequencing taxonomic analysis - Bioinformatic analysis of 16S rRNA sequencing data
- Amplicon sequencing functional prediction
- Shotgun metagenomic sequencing - Introduction to metagenomics
- Taxonomic analysis of shotgun metagenomic sequencing - IMPACTT 2022
- Functional analysis of shotgun metagenomic sequencing - IMPACTT 2022
- Construction of Metagenome Assembled Genomes (MAGs) from shotgun metagenomic sequencing data - Metagenome Analysis, Binning and Extracting Genomes
- Other - 16S sequencing vs shotgun metagenomics, Canadian Bioinformatics Workshops (series of tutorials from different years that the workshops were run)
- Introduction to Microbial 'Omics on the Meren Lab website (not a Youtube video)
- Patrick Schloss posts a weekly code club YouTube tutorial. These are aimed at some general topics in using R for data analysis, but are taught by a microbiome researcher and we think many are very useful. There are quite a lot available so we think it would be worth you working through at least some of them that seem like they would be of interest to you. Find a full list of them here
- Microbiome Helper Amplicon sequencing - this is the standard amplicon sequencing workflow that we use in the lab.
- QIIME2 tutorials - there are several different tutorials that you can work through on the QIIME2 website, with some toy data to use.
- Metagenome analysis tutorials/workshops
- Note that there are some further tutorials in the random section at the bottom of this page.
Here we have some papers that we think give a great overview of microbiome research and what a typical analysis workflow might look like as well as some papers that give a deeper dive on a particular topic. It is worth noting that as microbiome research is still a relatively new field (as we know it now, at least - leaning heavily on nucleic acid sequencing and bioinformatics for analysis). There are therefore several topics that are a bit contentious and many researchers disagree on the correct, or best, way to deal with them. In several cases, there are multiple methods/tools that are all designed for the same task. Researchers will also often have particular tools that they like using and may feel strongly about the use of different tools - or there may be tools that are possibly able to perform "better" than those, but if development isn't continued then these can often become very difficult to actually use (particularly for someone for which bioinformatics is only a small part of the methods that they use). My personal take on this is that the most important thing is that you should understand the methods you use, know why you are using those methods and also be aware of what the limitations of those methods are.
- Gavin M. Douglas & Morgan G.I. Langille (2021) A primer and discussion on DNA-based microbiome data and related bioinformatics analyses
- Rob Knight, Alison Vrbanac, Bryn C. Taylor, et al. (2018) Best practices for analysing microbiomes
- André M. Comeau, Gavin M. Douglas & Morgan G. I. Langille (2017) Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research
- QIIME2: Evan Bolyen, Jai R. Rideout, Matthew R. Dillon, et al. (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
- mothur: Patrick D. Schloss, Sarah L. Westcott, Thomas Ryabin, et al. (2009) Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities
- Patrick D. Schloss (2020) Reintroducing mothur: 10 Years Later
- Benjamin J. Callahan, Paul J. McMurdie, Michael J. Rosen, et al. (2016) DADA2: High-resolution sample inference from Illumina amplicon data
- Amnon Amir, Daniel McDonald, Jose A. Navas-Molina, et al. (2017) Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
- Jacob T. Nearing, Gavin M. Douglas, André M. Comeau & Morgan G.I. Langille (2018) Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches
- PICRUSt2: Gavin M. Douglas, Vincent J. Maffei, Jesse R. Zaneveld, et al. (2020) PICRUSt2 for prediction of metagenome functions
- Morgan G.I. Langille, Jesse Zaneveld, J. Gregory Caporaso, et al. (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
- Kraken: Derrick E. Wood & Steven L. Salzberg (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments
- Kraken2: Derrick E. Wood, Jennifer Lu & Ben Langmead (2019) Improved metagenomic analysis with Kraken 2
- MetaPhlAn: Nicola Segata, Levi Waldron, Annalisa Ballarini, et al. (2012) Metagenomic microbial community profiling using unique clade-specific marker genes
- BioBakery 3: Francesco Beghini, Lauren J. McIver, Aitor Blanco-Míguez, et al. (2021) Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3
- Anvi'o: A. Murat Eren, Evan Kiefl, Alon Shaiber, et al. (2020) Community-led, integrated, reproducible multi-omics with anvi’o
- POMs: Gavin M. Douglas, Molly G. Hayes, Morgan G.I. Langille, et al. (preprint) Integrating phylogenetic and functional data in microbiome studies
- Jacob T. Nearing, André M. Comeau & Morgan G.I. Langille (2021) Identifying biases and their potential solutions in human microbiome studies
- Gregory B. Gloor, Jean M. Macklaim, Vera Pawlowsky-Glahn & Juan J. Egozcue (2017) Microbiome Datasets Are Compositional: And This Is Not Optional
- Paul J. McMurdie & Susan P. Holmes (2014) Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
- Jacob T. Nearing, Gavin M. Douglas, Molly G. Hayes, et al. (2022) Microbiome differential abundance methods produce different results across 38 datasets
- Sophie Weiss, Zhenjiang Zech Xu, Shyamal Peddada, et al. (2017) Normalization and microbial differential abundance strategies depend upon data characteristics
- Robyn J. Wright, André M. Comeau & Morgan G.I. Langille (preprint) From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools
- Alexander Sczyrba, Peter Hofmann, Peter Belmann et al. (2017) Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software
- Fernando Meyer, Adrian Fritz, Zhi-Luo Deng, et al. (2022) Critical Assessment of Metagenome Interpretation: the second round of challenges - you can read a summary of the discussion our DalMUG journal club had about the preprint of this paper here.
- Writing a scientific article for beginners
- Resources for grad students (Twitter thread)
- Coding Club data science for ecologists and also here
- Impressive plots made with ggplot (Twitter thread)
- Writing a good introduction
- Examples of academic/personal websites (Twitter thread) and more examples of academic/personal websites (another Twitter thread) and more examples of academic/personal websites (Twitter thread again) and examples of grad students websites (Twitter thread) and examples of student and postdoc websites (Twitter thread) and websites or apps for creating them (Twitter thread)
- Writing the discussion (Twitter thread)
- Free to read (I think?) Springer textbooks
- Matplotlib cheat sheet
- Practical Python programming course
- Writing a response to reviewers
- GIVE: A Framework of Assumptions for Constructive Review Feedback
- Basics of R for ecologists (YouTube)
- R cheat sheets
- Classes on data visualisation in R and more classes on data visualisation in R
- Academic language (Twitter thread)
- R Studio keyboard shortcuts (Twitter thread)
- How to write a cover letter for manuscript submission (Twitter thread)
- Choosing a colour palette (Twitter thread)
- Introduction to scripting
- Resources for learning bioinformatics (Twitter thread)
- Using ggtree workshop
- Useful one-liners for bioinformatics
- Another R for ecologists course
- Bioinformatics data skills textbook (not free and I have not personally read it, just seen it recommended)
- How to tell a compelling story in scientific presentations and best practices for giving scientific presentations (Twitter thread)
- Designing posters (Twitter thread)
- Tips on making discussions better (Twitter thread)
- Website for deciding on the best visualisation for your data
- Introduction to bioinformatics and computational biology course
- R tidyverse bootcamp
- Intro to programming in Python lecture notes
- Preparing to give a talk (Twitter thread)
- ggplot advent CalendaR
- Common writing mistakes (Twitter thread)
- ChatGPT for those who write code (Twitter thread)
- R packages for data visualisation
- Data Viz inspiration
- Please feel free to post a question on the Microbiome Helper google group if you have any issues.
- General comments or inquires about Microbiome Helper can be sent to [email protected].