Quality Control of NGS Data
3 likes2,596 views
This document outlines exercises for quality control of NGS data from an Illumina sequencing experiment on tomato ripening stages. The exercises include: 1) evaluating raw fastq files for format and number of sequences; 2) using FastQC to analyze read quality scores, lengths, duplication levels, and k-mer content; and 3) preprocessing the reads using fastq-mcf to trim low quality ends and remove short reads before reanalyzing with FastQC. The goal is to learn how to evaluate NGS read quality and preprocess data prior to downstream analysis.
Quality Control of NGS Data
- 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 7/8/2014 BTI PGRP Summer Internship Program 2014 3 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 7/8/2014 BTI PGRP Summer Internship Program 2014 4 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 7/8/2014 BTI PGRP Summer Internship Program 2014 5 Slide credit: Aureliano Bombarely
- 6. Evaluation: Sequence Quality Good Illumina dataset 7/8/2014 BTI PGRP Summer Internship Program 2014 6
- 7. Evaluation: Sequence Quality 7/8/2014 BTI PGRP Summer Internship Program 2014 7 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 7/8/2014 BTI PGRP Summer Internship Program 2014 9
- 10. Evaluation: Sequence Content 7/8/2014 BTI PGRP Summer Internship Program 2014 10 Good Illumina dataset Poor Illumina dataset
- 11. Evaluation: Duplication Good Illumina dataset 7/8/2014 BTI PGRP Summer Internship Program 2014 11
- 12. Evaluation: Duplication 7/8/2014 BTI PGRP Summer Internship Program 2014 12 Good Illumina dataset Poor Illumina dataset
- 13. Evaluation: Overrepresented Sequences Good Illumina dataset 7/8/2014 BTI PGRP Summer Internship Program 2014 13
- 14. Evaluation: Overrepresented Sequences 7/8/2014 BTI PGRP Summer Internship Program 2014 14 Good Illumina dataset Poor Illumina dataset
- 15. Evaluation: Kmer content Good Illumina dataset 7/8/2014 BTI PGRP Summer Internship Program 2014 15
- 16. Evaluation: Kmer content 7/8/2014 BTI PGRP Summer Internship Program 2014 16 Good Illumina dataset Poor Illumina dataset
- 17. Evaluation: Kmer content 7/8/2014 BTI PGRP Summer Internship Program 2014 17 454 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. 7/8/2014 BTI PGRP Summer Internship Program 2014 18
- 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 7/8/2014 BTI PGRP Summer Internship Program 2014 19
- 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 7/8/2014 BTI PGRP Summer Internship Program 2014 20
- 21. Need Help?? 7/8/2014 BTI PGRP Summer Internship Program 2014 21 Solutions: https://bitly.com/BioinfoInternExSol2014