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QC IV Quality filtering
Once we now how the quality of our samples is, we have an idea of what kind of filtering or trimming we need to do.
For this exercise, we will be using data from the AS_2 sample (exome data). To make things faster we will focus only on chr21.
Fill in the table so we can make a comparison of methods
Copy chr21_dna_R1.fastq.gz and chr21_dna_R2.fastq from /home/teacher3/Fastq
There are several protocols to follow in order to leave only good quality reads. And it is recommended to perform this step even when the data looks fine. Again, there are different tools to do this, some are specific for certain types of data and some others are quite general.
Let's run some of them trying to use the same options (whenever possible) so our comparison is as fair as possible.
This a collection of command line tools that preprocess FASTA/FASTQ files. We can generate graphs, remove adapters, trim based on quality, etc.
Create a directory called Filter_fastx and save the results from this section there.
Make use of: fastx_clipper, fastq_quality_filter and fastq_quality_trimmer. Use AGATCGGAAGAGC as an adapter sequence.
Have a look a the files created and fill in the table.
This is a wrapper for quality and adapter trimming, and makes use of Cutadapt and FastQC on the fly.
Create a directory called Filter_galore and save the results from this section there.
Run $trim_galore with --fastqc so you get the FastQC plots. Have a look a the files created and fill in the table.
This is a tool that generates summary statistics of sequence and quality data and is used to filter, reformat and trim next-generation sequence data.
Create a directory called Filter_prinseq and save the results from this section there.
Run $prinseq_lite. Have a look a the files created and fill in the table.
Remember that you can use multiqc if you want to compile all the results.
Q. Was there any improvement in the quality of the reads? Which tool do you think performed better?
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Created by Marcela Dávila