1. Introduction to Data Processing and Variant Detection for NGS DNA Sequencing
EMC Galaxy Course
November 24-25, 2014
Youri Hoogstrate, David van Zessen, Saskia Hiltemann
Guido Jenster, Andrew Stubbs

2. How does next-gen sequencing work?

3. Instruments generate short reads that need to be mapped to the reference

4. High-level overview of NGS data processing

5. Aligned reads
In Galaxy, you can view your data in the built-in genome browser, Trackster

6. Challenge: distinguishing variants from noise
Possible reasons for a mismatch: - True SNP - Error generated in library prep - Base calling error - Misalignment (mapping error) - Error in reference genome

7. Genotyping
- What are the set of alleles at this locus? What are the frequencies? - Genotypers begin with a model of prior knowledge about the likelihood (and types) or errors, and the likelihood of observing real variants. - Error models depend on sequencing technology

8. What we know about NGS technology
Relatively high per-base error rate
Reads are higher quality in the middle than at the ends
Some technologies are poor with homopolymers, GC rich
Indels confuse alignment
Sequence coverage is not uniform
Alignments are probabilistic
Quality Control
Local realignment
Remove duplicate reads
Filter low-quality reads
Recalibrate base qualities
Read trimming

9. Quality Score Fastq: raw reads with per-base quality scores
Quality = Phred score + 33 (so that all characters are printable) Q= -10 log P (P= base-calling error probability) Q=10 error rate 10% Q=20 error rate 1% Q=30 error rate 0.1% etc..

10. Quality Control
Tool: FastQC

11. Sequencing Depth
Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data. BMC Genomics 2012, 13(Suppl 2):S6

12. Tools
Popular Tools: - SAM Tools Mpileup (practical) - GATK Unified Genotype Caller (practical extra part) - FreeBayes (practical extra part) - MAQ - Varscan2
All available in Galaxy Tool Shed
Always a trade-off between sensitivity and specificity; false positives and false negatives

13. Practical Raw data (fastq files) QC with FastQC Map with BWA
Visualize with Trackster
Call Variants with Mpileup
Annotate variants with ANNOVAR
Time permitting: Call Variants with FreeBayes and GATK Unified Genotyper and compare the three callers

14. Practical Session
Learn by doing it yourself! Servers: galaxy-training1.trait-ctmm.cloudlet.sara.nl galaxy-training2.trait-ctmm.cloudlet.sara.nl galaxy-training3.trait-ctmm.cloudlet.sara.nl
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All handouts and slides can be found under Shared Data → Data Libraries Manual: [Course Manual] EMC Galaxy Training 2: Introduction to Galaxy.pdf