1. Workshop on Microbiome and Health
Hands on: Metagenomics data types, statistics and quality control
Esteban Pérez Wohlfeil & Oswaldo Trelles
{estebanpw,
Computer Architecture Department, University of Malaga, Spain
Faculte des Sciences; Universite Sidi Mohamed Ben Abdellah
2017

2. Global Agenda Contents and time distribution
AGENDA (1h 00m)
Getting to know our Virtual Machine
Interacting and exploring the metagenomic samples
Quality control step using QTrim
Running a sequence comparison with a reference database using BLAST

3. Getting to know the Virtual Machine
The provided VM has Ubuntu and several software already installed to facilitate the hands-on.
The following software is incorporated already:
BLAST suite: BLASTn, BLASTx, BLASTp, …
MEGAN
Qtrim
Trimmomatic
METAGECKO
EMBOSS toolkit
Rstudio with R 3.3.3
Several scripts: FastaQ to Fasta converter, spreadsheets, plotting tools, etc.

4. Getting to know the Virtual Machine
Log into the “metagenomics-pipeline” user with the password: student

5. Getting to know the Virtual Machine
Examples that we will use during the hands-on are located in /home/student/Documents/Example

6. Getting to know the Virtual Machine
These examples include:
Folder 454:
Lean_TS1.fastq
Obese_TS19.fastq
Folder calc:
Spreadsheet to calculate differential abundance
Folder database:
A reference database containing several genomes commonly found in gastrointestinal human system
Folder results:
Empty folder to store processed files

7. Exploratory analysis

8. ? Exploratory Analysis FastQ Fasta
A metagenome can be seen as a long signal (often incomplete and noisy) that requires processing in order to detect anything significant
Although not mandatory, it is very recommended that we take a look into our samples always before starting a processing pipeline
FastQ
Fasta ?

9. Exploratory Analysis
In your Virtual Machine, start by opening a terminal clicking on the black command prompt in the left tab

10. Exploratory Analysis
We can execute commands on the terminal just as if we were double clicking on programs.
Lets first open up our metagenomes using the less command. Do as follows:
This will open up the lean_TS1.fastq metagenome. Does it look ok?
To navigate through the metagenome use the arrow keys.
To exit, just press q

11. Exploratory Analysis
(Skip this if you already know it) The terminal is a powerful tool to manage files. There are a few commands that always come in handy:
Command
Description
cp <file to copy> <destination>
Copies a file to another place
mv <file to move> <location to move>
Moves a file to another location
rm <file to delete>
Deletes a file
less <file to read>
Reads a text file in the terminal ls
Displays the contents of the folder
pwd
Shows the current working directory
cd <folder to enter>
Enters a folder. Use cd ../ to go back one level

12. Exploratory Analysis
Now we will check the distribution of lengths of the reads to see that there are no outliers. First convert from fastQ to fasta:
And then run the script exploratory.sh with the new fasta file as argument:
This will generate a .png image with a histogram of the distribution of length of reads.

13. Exploratory Analysis Are there any outliers?
Does it make sense taking into account the kind of sequencer it comes from?
Will it be different after the quality control step?
Before QC

14. Exploratory Analysis
We can also check the average length, the number of reads and the maximum length by opening the file that was generated automatically:

15. Quality Control Step
Quality control

16. Quality Control Step
Now we will perform the Quality Control step to trim and filter impurities in the samples. This goes from adapters to errors that have been included in the sequencing process.
Lets filter and trim both samples: lean_TS1.fastq and obese_TS19.fastq. To do so, execute the following commands into the terminal:
python ~/Qtrim/QTrim_v1_1/QTrim_v1_1.py -m 26 -fastq $DATA/454/lean_TS1.fastq -o $DATA/454/lean_TS1.trimmed.fastq
And also:
python ~/Qtrim/QTrim_v1_1/QTrim_v1_1.py -m 26 -fastq $DATA/454/obese_TS19.fastq -o $DATA/454/obese_TS19.trimmed.fastq

17. Quality Control Step
Remember to convert both of them to fasta format so we can run them in our pipeline:
And also:
This will generate the fasta files ready to be processed.

18. Quality Control Step
Now run the exploratory analysis for the new trimmed lean_TS1.trimmed.fasta file and compare previous and new plot
Are there any outliers?
Does it make sense taking into account the kind of sequencer it comes from?
Is it any different after the quality control step?
Before QC
After QC

19. Quality Control Step
Notes
Quality control should be rightly parametrized depending on the preparation libraries used in sequencing and the sequencing instrument
A strong biological knowledge is needed
Still, a filtering process will usually improve quality
The –M parameter can be adjusted for more/less filtering