# Analyses of Amplicon Sequences

#### [Sabah Ul-Hasan](https://github.com/sul-hasan) (@s_ulhasan), [Mike Lee](https://github.com/AstrobioMike) (@astrobiomike), and [Torsten Seeman](https://github.com/tseemann) (@torstenseemann)

- ANGUS 2018 [schedule](http://angus.readthedocs.io/en/2018/)
- A recap on using [amplicon vs metagenomics informatics tools](https://hackmd.io/bXyY0ttZSGaIFcnvwdKEVw?view)
- Tutorial on [assembling a metagenome](https://angus.readthedocs.io/en/2018/metagenomics_anvio.html)

### More than tools
Tools do help in speeding along the results interpretation process, but the question is ultimately the most important. The first step in the data analyses process is identifying a testable question or hypothesis. Then, what tools are most appropriate for answering that question and why? Next, are we including all the appropriate statistical tests, and do we understand the statistical tests being used? These statistics often go hand-in-hand with the data being visualized.


- A [recap on stastics](https://github.com/ngs-docs/angus/blob/2018/StatsNstuff.pdf) (Amanda Charbonneau)
- [Festing and Nevalainen 2014](https://academic.oup.com/ilarjournal/issue/55/3)
- [Buttigieg and Ramette 2014](https://onlinelibrary.wiley.com/doi/full/10.1111/1574-6941.12437)
- On [stastitical power and significance](https://www.nature.com/articles/nrg3706)
- On [sample size](https://www.nature.com/articles/nmeth.2738)
- On [statistical errors and the p value](https://www.nature.com/news/scientific-method-statistical-errors-1.14700)

### Amplicon sequence analysis tutorials
#### **What to use and when to use it?**
There are many discussions on using different tools over others. Below is a list of several. The best strategy is to (i) first familiarize yourself with one toolkit, (ii) then compare the output of the same dataset to outputs of other pipelines.

#### **Testing it out with a practice dataset**
There are many publicly available through the Joint Genome Institute. Feel free to use [Sabah's host-microbe raw pilot dataset](https://genome.jgi.doe.gov/portal/Sheligcoanalysis/Sheligcoanalysis.info.html), filled with all sorts of troubleshooting fun.

* **usearch/vsearch** (ASVs and OTUs) – command line
* [usearch](https://www.drive5.com/usearch/) is not entirely free, but it has some very useful tools and approaches (e.g. good calculation of hybrid quality scores after merging overlapping reads). There is a lightweight free version that can still do many things.
* [vsearch](https://github.com/torognes/vsearch/wiki/VSEARCH-pipeline) is completely free and open, and was made in response to usearch not being completely free. It does not have all of the capabilities of usearch however.
* Mike's [usearch/vsearch tutorial](https://astrobiomike.github.io/amplicon/workflow_ex)

* **dada2** (ASVs) - R
* [Developer tutorial](https://benjjneb.github.io/dada2/tutorial.html)
* Mike's [dada2 tutorial](https://astrobiomike.github.io/amplicon/dada2_workflow_ex): A java installation is needed along with the dada2 R package, both of which aren't straightforward on our cloud. Here's on approach for getting them going (you can paste in this block of code below at the command line after logging into your loud instance)

  ## installing java at the command line ##

cd
wget --continue --no-cookies --no-check-certificate --header "Cookie: oraclelicense=accept-securebackup-cookie" "http://download.oracle.com/otn-pub/java/jdk/10.0.1+10/fb4372174a714e6b8c52526dc134031e/jdk-10.0.1_linux-x64_bin.tar.gz" -O jdk-10-linux-x64.tar.gz
tar -xzvf jdk-10-linux-x64.tar.gz
echo 'export JAVA_HOME=/home/diblions/dada2_amplicon_ex_workflow/jdk-10.0.1' >> ~/.bash_profile
echo 'export PATH=/home/diblions/dada2_amplicon_ex_workflow/jdk-10.0.1/bin:$PATH' >> ~/.bash_profile
echo `source ~/.bashrc` >> ~/.bash_profile
source ~/.bash_profile

  ## installing dada2 R package ##
      # this needs to be done at the command line too, not inside RStudio
      
R # this will open R at the command line, and your prompt will change from the `$` to `>`
source("https://bioconductor.org/biocLite.R") # this will connect to bioconductor where we get the package from
biocLite("dada2") # installs the package

    ## follow the directions here to be able to open RStudio from the cloud:
## https://angus.readthedocs.io/en/2018/visualizing-blast-scores-with-RStudio.html#getting-started

* **[mothur developer tutorial](https://www.mothur.org/wiki/MiSeq_SOP)** (OTUs) - command line
* **[qiime2 developer tutorial](https://docs.qiime2.org/2018.6/tutorials/moving-pictures/)** – command line
* qiime provides an environment that "wraps" (employs) other processing tools (like those above) and also provides convenient visualization capabilities
* Using [qiime2 in R](https://rdrr.io/github/jbisanz/qiime2R/f/README.md)
* R tutorials for statistics and visualization
* Different step by step example datasets
* [Maggi Brisbin](https://maggimars.github.io/RedSoil/CommunityAnalysis.html)
* [Mike Lee](https://astrobiomike.github.io/amplicon/workflow_ex#analysis-in-r)
* [Sabah Ul-Hasan](https://github.com/sul-hasan/MicrobialCommunityAnalysesPipeline_Supplemental_SUHetal)
* [microbiomeSeq](http://userweb.eng.gla.ac.uk/umer.ijaz/projects/microbiomeSeq_Tutorial.html) R package
* [RAM](https://rdrr.io/cran/RAM/man/RAM-package.html) R package
* [Rhea]((https://peerj.com/articles/2836/)) R package
* [microPop](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12873) R package
* [phyloseq](https://joey711.github.io/phyloseq/) R package

### Additional Resources
https://github.com/bowmanjeffs/paprica
https://www.wolframalpha.com/
https://galaxyproject.org/main/
http://phinch.org/
http://www.cytoscape.org/
https://www.coursera.org/learn/datavisualization
https://github.com/miltontan/miltontan.github.io
https://pages.github.com/