Quality Control of NGS Data

Surya Saha [email protected]

BTI PGRP Summer Internship Program 2014

Slides: https://bitly.com/BioinfoInternEx2014

Quality Control of NGS Data

mailto:[email protected]

https://bitly.com/BioinfoInternEx2014

https://bitly.com/BioinfoInternEx2014

1. Evaluation

2. Preprocessing

Quality Control of NGS Data

7/8/2014 BTI PGRP Summer Internship Program 2014 2 Slide credit: Aureliano Bombarely

Goal:

Learn the use of read evaluation programs keeping

attention in relevant parameters such as quality score and

length distributions and reads duplications.

Data: (Illumina data for two tomato ripening stages)

/home/bioinfo/Data/ch4_demo_dataset.tar.gz

Tools: tar -zxvf (command line, untar and unzip the files)

head (command line, take a quick look of the files)

mv (command line, change the name of the files)

grep (command line, find/count patterns in files)

FASTX toolkit (command line, process fasta/fastq)

FastQC (gui, to calculate several stats for each file)

Evaluation


Exercise 1:

1. Untar and Unzip the file:

/home/bioinfo/Data/ch4_demo_dataset.tar.gz

2. Raw data will be found in two dirs: breaker and

immature_fruit. Print the first 10 lines for the files:

SRR404331_ch4.fq, SRR404333_ch4.fq,

SRR404334_ch4.fq and SRR404336_ch4.fq.

Question 1.1: Do these files have fastq format?

3. Change the extension of the .fq files to .fastq

Evaluation


Exercise 1:

4. Count number of sequences in each fastq file using

commands you learnt earlier.

5. Convert the fastq files to fasta.

6. Explore other tools in the FASTX toolkit.

7. Now count the number of sequences in fasta file and see

if the number of sequences has changed.

Evaluation

Tip: Use ‘grep’

Tip: Use ‘fastq_to_fasta -h’ to see help Use Google if you are stuck


http://hannonlab.cshl.edu/fastx_toolkit/commandline.html

Evaluation: Sequence Quality

Good Illumina dataset

7/8/2014 BTI PGRP Summer Internship Program 2014 6




Poor Illumina dataset



454

Pacific Biosciences

Evaluation: Sequence Content



Evaluation: Sequence Content




Evaluation: Duplication



Evaluation: Duplication




Evaluation: Overrepresented Sequences



Evaluation: Overrepresented Sequences




Evaluation: Kmer content









454

Pacific Biosciences

Question 2.2: How many sequences there are per file in FastQC?

Question 2.3: Which is the length range for these reads?

Question 2.4: Which is the quality score range for these reads? Which

one looks best quality-wise?

Question 2.5: Do these datasets have read overrepresentation?

Question 2.6: Looking into the kmer content, do you think that the samples

have an adaptor?

Evaluation Exercise 2:

1.Type ‘fastqc’ to start the FastQC program. Load the four

fastq sequence files in the program.


Goal:

Trim the low quality ends of the reads and remove

the short reads.

Data: (Illumina data for two tomato ripening stages)

ch4_demo_dataset.tar.gz

Tools: fastq-mcf (command line tool to process reads)

FastQC (gui, to calculate several stats for each file)

Preprocessing


Exercise 3:

• Download the file: adapters1.fa from ftp://ftp.solgenomics.net/user_requests/aubombarely/courses/RNAseqCorpoica/a

dapters1.fa

• Run the read processing program over each of the datasets

using

• Min. qscore of 30

• Min. length of 40 bp

• Type ‘fastqc’ to start the FastQC program. Load the four

new fastq sequence files. Compare the results with the

previous datasets.

Preprocessing

Tip: Use ‘fastqc -h’ to see help


ftp://ftp.solgenomics.net/user_requests/aubombarely/courses/RNAseqCorpoica/adapters1.fa



Need Help??


Solutions: https://bitly.com/BioinfoInternExSol2014

Quality Control of NGS Data

Education

fastqc program

fastq evaluation

file evaluation

evaluation exercise

evaluation tip

read processing program

evaluation programs

fastq files