Metabarcoding analysis

  • Please log into the VM using the given credentials.
    • The hostname is quadram.seq.space.
    • The username is bio0X, where X will be a number provided via Skype
    • The password is Norwich0X, where X will be a number provided via Skype
  • You can install tools using Miniconda, and (if needed) you can write small scripts using your favourite editor.
  • The session is monitored and logged
  • :!: Should you wish to skip a step, you'll find the pre-computed output in /media/data/precomputed/16S/.

This test will ask you to perform some computations related to 16S data, using a simplified and unrealistic protocol.

Explore your data

In the shared directory /media/data/16S/ you will find a metadata file and a set of reads.

  • Examine the metadata file and check what is the link between the metadata and the read filenames in /media/data/16S/.
  • Create a directory in your home called ~/16S/reads.
  • For each stool sample, take 5,000 reads and place them in ~/16S/reads.
  • Create a subdirectory merged inside your 16S folder (i.e. ~/16S/merged).
  • The read pairs for each sample overlap, so please merge them using a tool such as FLASH or USEARCH, saving the output to ~/16S/merged.

Pre-computed steps

Some steps were made for you, You will find the output of these steps in ~/output/.

  1. Create a single file with all the reads from all of the samples produced in the last step (call it ~/16/merged/all.fastq), relabeling each read name to begin with the sample name (filename prefix) and a progressive number. You can use the dot as separator.
  2. Create an OTU table by running the command below:
    merged_to_otus ~/16/merged/all.fastq <output_directory>

Now:

  • Extract the sequences named OTU1, OTU2, OTU3, OTU4, OTU5, from the output (otus.fa) of the previous step and save it as ~/16S/five.fasta.

Numerical analysis

Using R or Python (Pandas):

  1. Calculate the sum of counts of each sample (column) of the otutab.txt and otutab.raw files.
  2. Create a stacked bar chart of the OTU composition of each sample from otutab.txt.
  3. Create a otutable.sorted.txt file, sorting the table by total otu abundance (sum the counts in all samples)
  4. Extract the top 5 most abundant OTUs from the OTU database and save them as ~/16S/top5.fa