Quality trimming Illumina paired-end reads¶
In this excercise you will learn how to quality trim Illumina paired-end reads. The most common Next Generation Sequencing (NGS) technology for metagenomics.
For quality trimming Illumina paired end reads we use the library sickle which trims reads from 3’ end to 5’ end using a sliding window. If the mean quality drops below a specified number the remaining part of the read will be trimmed.
Downloading a test set¶
Today we’ll be working on a small metagenomic data set from the anterior nares (http://en.wikipedia.org/wiki/Anterior_nares).
So get ready for your first smell of metagenomic assembly - pun intended. Run all these commands in your shell:
# Download the reads and extract them mkdir -p ~/asm-workshop mkdir -p ~/asm-workshop/data cd ~/asm-workshop/data wget http://downloads.hmpdacc.org/data/Illumina/anterior_nares/SRS018585.tar.bz2 tar -xjf SRS018585.tar.bz2
If successfull you should have the files:
$ ls -lh ~/asm-workshop/data/SRS018585/ -rw-rw-r-- 1 inod inod 36M Apr 18 2011 SRS018585.denovo_duplicates_marked.trimmed.1.fastq -rw-rw-r-- 1 inod inod 36M Apr 18 2011 SRS018585.denovo_duplicates_marked.trimmed.2.fastq -rw-rw-r-- 1 inod inod 6.4M Apr 18 2011 SRS018585.denovo_duplicates_marked.trimmed.singleton.fastq
If not, try to find out if one of the previous commands gave an error.
Look at the top of the one of the pairs:
cat ~/asm-workshop/data/SRS018585/SRS018585.denovo_duplicates_marked.trimmed.1.fastq | head
Question: Can you explain what the different parts of this header mean @HWI-EAS324_102408434:5:100:10055:13493/1?
Running sickle on a paired end library¶
I like to create directories for specific parts I’m working on and creating
symbolic links (shortcuts in windows) to the input files. One can use the
ln for creating links. The difference between a symbolic link and a
hard link can be found here:
In this case I use symbolic links so I know what path the original reads have,
which can help one remember what those reads were:
mkdir -p ~/asm-workshop/sickle cd ~/asm-workshop/sickle ln -s ../data/SRS018585/SRS018585.denovo_duplicates_marked.trimmed.1.fastq pair1.fastq ln -s ../data/SRS018585/SRS018585.denovo_duplicates_marked.trimmed.2.fastq pair2.fastq
Now run sickle:
# check if sickle is in your PATH which sickle # Run sickle sickle pe \ -f pair1.fastq \ -r pair2.fastq \ -t sanger \ -o qtrim1.fastq \ -p qtrim2.fastq \ -s qtrim.unpaired.fastq # Check what files have been generated ls
Sickle states how many reads it trimmed, but it is always good to be
suspicious! Check if the numbers correspond with the amount of reads you count.
Question: How many paired reads are left after trimming? How many singletons?
Question: What are the different quality scores that sickle can handle? Why do we specify -t sanger here?