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Quality Control of Sequencing Data
Surya Saha
Sol Genomics Network (SGN)
Boyce Thompson Institute, Ithaca, [email protected] // 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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