Quality Control and Preprocessing Exercise

Aims

NGS can be affected by a range of artefacts that arise during the library preparation and sequencing processes including low base quality, contamination with adapter sequences and biases in base composition, which can negatively impact the quality of the raw data for downstream analyses. In this part you will learn to: assess the intrinsic quality of raw reads using metrics generated by the sequencing platform (e.g. quality scores) pre-process data, i.e. trimming the poor quality bases and adapters from raw reads You will use the following tools, which have been pre-installed on our bioinformatics training server at the University of Edinburgh: FastQC: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ FastqMcf: https://code.google.com/p/ea-utils/wiki/FastqMcf The data set you'll be using is downloaded from ENA (http://www.ebi.ac.uk/ena/data/view/SRP019027). The reads belong to sample SRR769316. The data set is tailored with respect to the time allocated for the workshop.

Type text like this in the terminal at the $ command prompt, then press the [Enter] key to run the command.

View data set

cd /home/training/Data/03_Quality_control_and_data_preprocessing
head Read_1.fastq
head Read_2.fastq

Assessment of data quality

Run FastQC on the raw data:

fastqc --nogroup Read_1.fastq Read_2.fastq
firefox Read_*_fastqc.html &

where:

• --nogroup disables grouping of bases for reads >50bp. All reports will show data for every base in the read.

Look at the FastQC results and answer the following questions:

• What is the quality encoding?
• How many reads are present in each fastq file?
• What is the length of the reads?
• Are there any adapter sequences observed?
• Which parameters you think should be used for trimming the reads?

Pre-processing of data

Trim reads using Fastq-Mcf:

fastq-mcf -o Read_1_q30l50.fastq -o Read_2_q30l50.fastq \
-q 30 -l 50 \
--qual-mean 30 adapters.fasta Read_1.fastq Read_2.fastq

where:

• -o output file
• -q quality threshold causing base removal
• -l Minimum remaining sequence length
• --qual-mean - Minimum mean quality score

Reassessment of data quality

Run FastQC on the trimmed reads:

fastqc --nogroup Read_1_q30l50.fastq Read_2_q30l50.fastq
firefox Read*q30l50*fastqc.html

Look at the FastQC results and answer the following questions:

• How many reads are present in each fastq file?
• What is the length of the reads?
• Did qualities improve?