1. From Reads to Results Exome-seq analysis at CCBR
Justin Lack
March 8, 2015

2. Workflow for Data Analysis
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation

3. Workflow for Data Analysis
FASTQ format
QC analysis
Read trimming
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation

4. FASTQ Data Format FASTQ format Sequence ID Sequence Quality score
Phred-scaled quality value – i.e., Q10 mean 1/10 error rate, Q20 means 1/100, etc.
Sequence ID
Sequence
Quality score

5. Read Quality Assessment
Read quality analysis
Crucial to ensure high quality data
Can reveal issues in library preparation and sequence generation

6. Read Quality Assessment
Read trimming
Trims reads for both adapter contamination and low quality
Absolutely essential for variant detection

7. Read Quality Assessment
FastQC and trimming (Trimmomatic)

8. Workflow for Data Analysis
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation
Map reads to reference genome
Alignment QC

9. Read Mapping Challenge:
compare billions of short sequence reads against human genome (3Gb)

10. Different Alignment Algorithms
BWA – 2009
BWA-SW – 2010
BWA-MEM – 2013
Bowtie – 2009
Bowtie2 – 2012
Gem – 2012
Cushaw2 – 2014
Novoalign
Li, arXiv: (2013)

11. Different Alignment Algorithms
BWA – 2009
BWA-SW – 2010
BWA-MEM – 2013
Bowtie – 2009
Bowtie2 – 2012
Gem – 2012
Cushaw2 – 2014
Novoalign
Li, arXiv: (2013)

12. SAM/BAM Format SAM (Sequence Alignment/Map) format
Single unified format for storing read alignments to a reference genome
BAM (Binary Alignment/Map) format
Binary equivalent of SAM
Advantages
Supports indexing
Compact size

13. BAM File Format
Header
Data

14. Alignment QC
Crucial for examining and summarizing quality of alignment at exome targets
GATK Depth of Coverage

15. Alignment QC
Crucial for examining and summarizing quality of alignment at exome targets
Qualimap

16. BAM Visualization - IGV
Mismatches
Integrative Genomics Behavior
Reference

17. Workflow for Data Analysis
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation
- BAM/SAM Alignment improvement

18. BAM Improvement
Short-read mappers designed to balance accuracy and speed
Algorithm can result in errors, especially at challenging indels
Tools designed to target specific systematic errors
Remove duplicates
Local realignment
Base quality recalibration

19. Library Duplicates
All next generation sequencing platforms are NOT single molecule sequencing
PCR amplification step in library preparation
Can result in duplicate DNA fragments in the final library prep.
PCR-free protocols do exist – require large volumes of input DNA
Can result in false SNP calls
Duplicates manifest themselves as high read depth support

20. Duplicates and False SNP Calls

21. Remove Duplicates
Identify read-pairs where the outer ends map to the same position on the genome and remove all but 1 copy
Samtools: samtools rmdup or samtools rmdupse
Picard/GATK: MarkDuplicates

22. Local Realignment - indels
The trouble with mapping approaches

23. Local Realignment - indels
The trouble with mapping approaches

24. Local Realignment - indels
The trouble with mapping approaches

25. Local Realignment - indels

26. Local realignment in GATK
Uses information from known SNPs/indels (dbSNP, 1000 Genomes)
Uses information from other reads
Smith-Waterman exhaustive alignment on select reads
Similar to GATK Haplotype Caller

27. Quality scores issued by sequencers are inaccurate and biased
Quality  scores  are  critical  for  all  downstream  analysis
Systematic  biases  are  a  major  contributor  to  bad calls

28. Base Quality Recalibration
Sequence context refers to base composition skews

29. Base Quality Recalibration in GATK
Align subsample of reads from a lane to human reference
Exclude all known dbSNP sites
Assume all other mismatches are sequencing errors
Compute a new calibration table based on mismatch rates per position on the read

30. Base Quality Recalibration

31. Workflow for Data Analysis
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation
- Germline variant
detection
- Somatic variant detection
- VCF files

32. Germline Variant Detection
Mutations are hidden in the noise!

33. Germline Variant Detection
Mutations are hidden in the noise!
Utilize GATK Haplotype Caller

34. Germline Variant Detection
Mutations are hidden in the noise!
Utilize GATK Haplotype Caller
Genotype jointly to maximize information

35. Germline Variant Detection
Mutations are hidden in the noise!
Utilize GATK Haplotype Caller
Genotype jointly to maximize information

36. Somatic Variant Detection
Genes and chromosomes can mutate in either somatic or germline tissue
Mutation Detection

37. An Example of Germline Variants
Robinson et al. 2011

38. An Example of Somatic Variants
Normal
Tumor

39. Somatic Variant Detection
But somatic variant detection can be EXTREMELY difficult
Allelic fractions do not scale to ploidy

40. Somatic Variant Detection
But somatic variant detection can be EXTREMELY difficult
Multiple additional sources of errors
Low depth and/or tumor
contaminated normal
Noise vs Event

41. MuTect2
Somatic caller that attempts to account for and model all of these sources of errors

42. Variant Call Format (VCF)
VCF is a standardized format for storing DNA polymorphism data
SNPs, insertions, deletions and structural variants
With rich annotations
Indexed for fast data retrieval of variants from a range of positions
Store variant information across many samples
Record meta-data about the site
dbSNP accession, filter status, validation status,
Very flexible format

43. Example VCF

44. Workflow for Data Analysis
Read Generation
Read Mapping
BAM Processing
Variant Calling
Variant Annotation
- Genome Annotation Databases
- AVIA…

45. Annotation and Functional Prediction

46. dbSNP
dbSNP is a free public archive for genetic variation within and across different species developed by NCBI
Sherry, Genome Res. 1999

47. 1000 Genomes Project 15 million SNPs
1 million short insertions/deletions  
20,000 structural variants
The 1000 Genomes Project Consortium, Nature 2010 (

48. COSMIC
COSMIC is the most comprehensive resource for exploring impact of somatic mutations in human cancer
Forbes, Nucleic Acids Research 2015

49. COSMIC

50. Lots lots more in AVIA!

51. Thank you!
Any Questions?