BTI PGRP Summer Internship Program 2014

1. Surya Saha ss2489@cornell.edu
BTI PGRP Summer Internship Program 2014
Slides: https://bitly.com/BioinfoInternEx2014
Quality Control of NGS Data

2. 1. Evaluation
2. Preprocessing
Quality Control of NGS Data
7/8/2014 BTI PGRP Summer Internship Program 2014 2
Slide credit: Aureliano Bombarely

3. 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
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Slide credit: Aureliano Bombarely

4. 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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Slide credit: Aureliano Bombarely

5. 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
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Slide credit: Aureliano Bombarely

6. Evaluation: Sequence Quality
Good
Illumina
dataset
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7. Evaluation: Sequence Quality
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Good
Illumina
dataset
Poor
Illumina
dataset

8. Evaluation: Sequence Quality
7/8/2014 BTI PGRP Summer Internship Program 2014 8
454
Pacific
Biosciences

9. Evaluation: Sequence Content
Good
Illumina
dataset
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10. Evaluation: Sequence Content
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Good
Illumina
dataset
Poor
Illumina
dataset

11. Evaluation: Duplication
Good
Illumina
dataset
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12. Evaluation: Duplication
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Good
Illumina
dataset
Poor
Illumina
dataset

13. Evaluation: Overrepresented Sequences
Good
Illumina
dataset
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14. Evaluation: Overrepresented Sequences
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Good
Illumina
dataset
Poor
Illumina
dataset

15. Evaluation: Kmer content
Good
Illumina
dataset
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16. Evaluation: Kmer content
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Good
Illumina
dataset
Poor
Illumina
dataset

17. Evaluation: Kmer content
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454
Pacific
Biosciences

18. 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.
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19. 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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20. 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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21. Need Help??
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Solutions: https://bitly.com/BioinfoInternExSol2014