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Introduction to metagenomics.

Metagenomics is a technique to study microbial diversity using environmental DNA samples. The diversity can either be analyzed using gene-centric / amplicon methods (e.g. PCR fragements of the gene of interest) or via shotgun sequencing. During this course we will start with the bioinformatic analysis of an amplicon dataset. This will gives us a chance to get used to data clean up methods and get introduced to diversity analysis in R-studio with the phyloseq package. We will follow the Mothur MiSeq SOP for the analysis of illumina amplicon data.

The course program

  • Reducing sequencing and PCR errors.
  • Generating OTUs and analysis of diversity with Mothur.
  • R-studio / Phyloseq tutorial for diversity analysis.
  • Introduction to shotgun metagenomics.

Lecture 2018 – 5 – February

During this day we start with the Mothur SOP for MiSeq data and we will follow the tutorial using the Biolinux mothur version in our virtual box environment.

important note. You can work yourself through this tutorial, but note that the mothur version on your biolinux is not up to data with the latests mothur versions. This means that for a few commands we need to use an different command than is used in the mothur SOP. Below I will give a link to the commands that we used during the course. This also means that when you work through the tutorial you will not find the same numbers for reads kept or removed.

Preperation step for the first lecture.

  1. Create directory on the desktop in the Linux Virtual box called: amplicons

  2. Download and save the following datasets in the folder amplicons:

  3. extract zipped files using the commandline: unzip FILENAME. This creates two folders: 1) Miseq_SOP and 2) silva.bacteria. and two lose files.

  4. move the directory silva.bacteria to the Miseq_SOP folder: mv silva.bacteria/ Miseq_SOP/

  5. move the files Trainset* to the Miseq_SOP folder: mv Trainset* Miseq_SOP/

The tutorial steps

Open file finder window and move to folder amplicons, check the files that you have downloaded. Since we have a lot of fastq data and we are curious, we want to get an idea of the quality of the sequences. We can use the tool fastqc which is installed in the biolinux machine. To run this on all the MiSeq fastq datafiles of this tutorial do the following:

  1. Open terminal window, and change to the directory with the fastq files.
  2. create a directory called: fastqc-results with the mkdir command.
  3. next we run the command: fastqc -t 2 -o fastqc_results *.fastq , to understand thie flags in this command type: fastqc --help
  4. open firefox and open a fastqc results file: fastqc_report.html. You can find for each dataset a folder inside the folder: fastqc-results. For instance open the folder for the forward sequences: F3D0_S188_L001_R1_001_fastqc and check the results there.

Note that this is amplicon data, why is that different from normal shotgun data, and how is that observed in the fastqc file? See also these documents: Good sequences or Bad sequences

Next we open mothur an start working on the MiSeq SOP produced by Pat Schloss.

We work until the step where we remove chimeric sequences.

The commands can be found here: 20180205 MiSeq_SOP commands.

Recommended literature

  • Preclustering: Huse et al., Ironing out the wrinkles in the rare biosphere through improved OTU clusteringEnv. Microbiol 2010, 1889 – 1898

homework

Read the manuscript: Madness of Microbiome download here.

Lecture 12 - February - 2018

During this day we discus the manuscript from Pollock et al., 2018.

And we continue with the clean-up of the MiSeq SOP amplicon data. You can find the details of the mothur commands in this document 20180212 MiSeq_SOP commands.

Recommended literature

homework

Now that we have finished cleaning our dataset we can now put everything into a textfile, so we can analyse the data using the abel computing cluster.

Create a text file with all the mothur commands to clean-up your datafile.

You can test if this file works in your biolinux machine. The way to call the script is like this: mothur yourfile_with_commands.txt

For a little bit more info see the Mothur manual on batch mode

Next I want you to create a slurm script that can will run mothur with thr script on abel.

Use the slurm commands you learned during the introduction course to set-up a computing allocation on able that will run your mothur script with the commands. For more on that see the abel user guide.

Make sure you ask for the right mothur module and call the script with your commands from your working directory on abel.

Lecture 26 - February - 2018

We will start with the homework of the last time:

After that we will start with diversity analysis using mothur. Today, we focus on alpha diversity and calculate the rarefaction curves as well as several alpha diversity estimators.

The commands we used: 20180226 MiSeq SOP commands

Note! that the summary.single command in this file uses the final.shared file and not the final.0.03.subsample.sharedfile. This gives a better calculation of the standard deviation for the invsimpson index, as well as the other estimators.

homework

  • Clean up the file commands_edit.txtand test it on your biolinux machine to see it works correctly.
  • When the file is clean and works, then upload it to abel using rsync.
  • Next make sure all files needed for the MiSeq_SOP (Silva, rdp, fastq files) are present on abel
  • When complete, then run the slurm script we created to run mothur using the abel cluster.

Recommended literature

Lecture 7 - March - 2018

Today we made a script with mothur commands to process the MiSeq_SOP dataset, that could run on the biolinux machine. The script is here: commands_edit_v2.txt

Next we tested the script on abel using screen and a qlogin and detected an error due to chimera.uchime giving an output file with a slightly different name. That caused an error in the step to remove the chimeric sequences. We modified the file, and that file is found here: commands_edit_v3.txt

Here is a link to how to set-up an interactive job on abel.

Next we analyzed using R-studio the alpha-diversity data, by writing scripts to visualize the data.

After visualization we continue the mothur MiSeq_SOP analysis by exploring various options to study the betadiversity. Here is the list of commands we used:20180307_MiSeq_SOP_commands

Homework for monday 12 March 2018:

  • make your script with mothur commands functional, so you can run it on abel.
  • run the mothur commands file using a the run_mothur.slurm file
  • if it does not work, contact me via email.

Lecture 19 - March 2018

During this session, we will work on analyzing the Mothur MiSeq SOP data using the R-package Phyloseq using multivariate statistics. The workflow of this tutorial.

Lecture 17 - April 2018

This is a lecture to introduce the classification tool Kraken. This tool is going to be used for the proficiency test to identify which organisms were cultivated and sequenced. The Kraken tutorial.

Lecture 23 - April 2018

In this lecture we will download SILVA database, use the arb program to export a fasta file from the SILVA database, and curate the database with mothur. At the end we will generate a proper taxonomy file. The tutorial is found here:Building a 16S rRNA classification database.

Future Lectures

  • How to build classification databases.