Quality Control of Sequencing Data

Surya Saha

Sol Genomics Network (SGN)

Boyce Thompson Institute, Ithaca, NYss2489@cornell.edu // Twitter:@SahaSurya

BTI Plant Bioinformatics Course 2015

Slides: Aureliano Bombarely

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1. Evaluation

2. Preprocessing

Quality Control of NGS Data

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Goal:

Learn the use of read evaluation programs keeping

attention in relevant parameters such as quality score and

length distributions and unusual 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

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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

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Exercise 1:

4. Count number of sequences in each fastq file using

commands you learnt last time.

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 helpUse Google if you are stuck

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Evaluation: Sequence Quality

Good Illumina dataset

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Evaluation: Sequence Quality

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Good Illumina dataset

Poor Illumina dataset

Evaluation: Sequence Quality

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454

Pacific Biosciences

Evaluation: Sequence Content

Good Illumina dataset

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Evaluation: Sequence Content

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Good Illumina dataset

Poor Illumina dataset

Evaluation: Duplication

Good Illumina dataset

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Evaluation: Duplication

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Good Illumina dataset

Poor Illumina dataset

Evaluation: Overrepresented Sequences

Good Illumina dataset

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Evaluation: Overrepresented Sequences

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Good Illumina dataset

Poor Illumina dataset

Evaluation: Kmer content

Good Illumina dataset

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Evaluation: Kmer content

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Good Illumina dataset

Poor Illumina dataset

Evaluation: Kmer content

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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?

EvaluationExercise 2:

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

fastq sequence files in the program.

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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

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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

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