Data Analysis for Exome Sequencing Data

1. Data Analysis for Exome Sequencing Data Chih-Hao Hsu 03/18/2015

2. Workflow for Data Analysis Read Generation Read Mapping Variant Calling Annotation and Filtering Driver Mutations

3. Workflow for Data Analysis Read Generation - Store in a FASTQ file - QC study Read Mapping Variant Calling Annotation and Filtering Driver Mutations

4. Raw Sequence Data Format • FASTQ format • Phred quality score Sequence ID Sequence Quality score !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRS Phred score 0………………………………………………………………………………………….50 Error rate 1……………………………………………………………………………………..0.00001 Phred score = -10 * log10P

5. Sequence quality: FastQC • http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

6. Workflow for Data Analysis Read Generation - Map reads to reference genome - Different aligners - SAM/BAM file - BAM improvement Read Mapping Variant Calling Annotation and Filtering Driver Mutations

7. Read Mapping • Challenge: • compare billion of short sequence reads against human genome (3Gb) • Burrows-Wheeler Alignment tool (BWA) • Popular tool for genomic sequence data • “index” the human genome to allow memory-efficient and fast string matching between sequence read and reference genome

8. Different Alignment Algorithms • BWA – 2009 • BWA-SW – 2010 • BWA-MEM – 2013 • Bowtie – 2009 • Bowtie2 – 2012 • Gem – 2012 • Cushaw2 – 2014 • Novoalign Li, arXiv:1303.3997 (2013)

9. 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

10. 1000 Genomes BAM File Header Data

11. BAM Visualization Mismatches Reference

12. BAM Improvement • Remove duplicates • Local realignment • Base quality recalibration

13. Library Duplicates • All second-gen 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

14. Duplicates and False SNP Calls

15. 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

16. Local Realignment - indels • The trouble with mapping approaches

17. Local Realignment - indels • The trouble with mapping approaches

18. Local Realignment - indels • The trouble with mapping approaches

19. Local Realignment - indels Local realignment

20. 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

21. The 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 https://www.broadinstitute.org/gatk/

22. 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

23. Workflow for Data Analysis Read Generation Read Mapping Variant Calling - SNP calling - Short Indels - Structural Variation - Germline vs. Somatic - VCF files Annotation and Filtering Driver Mutations

24. Variant Calling Differences to the reference Reference: C Sample: T

25. Signal vs. Noise Sanger: is it real?? Total count: 204 A : 18 (9%, 12+, 6-) C : 1 (0%, 0+, 1-) G : 0 T : 185 (91%, 92+, 93-) N : 0 NGS: read count Provides confidence (statistics!) Sensitivity tune-able parameter (dependent on coverage)

26. Variant Calling • SNP calling • Short Indels • Structural Variation

27. SNP Calling • SNP – single nucleotide polymorphisms • Examine the bases aligned to position and look for differences • Factors to consider when calling SNPs • Base call qualities of each supporting base • Proximity to small indel • Mapping qualities of the reads supporting the SNP • Read length • Paired reads • Sequencing depth

28. Example SNP http://www.sanger.ac.uk/mousegenomes

29. Is this a SNP? http://www.sanger.ac.uk/mousegenomes

30. Short indel Calling • Small insertions and deletions observed in the alignment of the read relative to the reference genome • Factors to consider when calling indels • Misalignment of the read • Homopolymer runs either side of the indel • AAAA or TTTTTTTT • Length of the reads

31. Example Indel http://www.sanger.ac.uk/mousegenomes

32. Is this a Indel? http://www.sanger.ac.uk/mousegenomes

33. Germline vs. Somatic Variants • Genes and chromosomes can mutate in either somatic or germline tissue Mutation Detection

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

35. An Example of Somatic Variants Normal Tumor

36. Different Variants Callers Ding, Nat Rev Genet. 2014

37. The GATK software • Genome Analysis Toolkit, BROAD Institute http://www.broadinstitute.org/gatk/ • Initially developed for 1000 Genomes Project • Single or multiple sample analysis (cohort) • Popular tool for germline variant calling

38. Somatic Variant Calling • Somatic mutations can occur at low freq. (<10%) due to: • Tumor heterogeneity (multiple clones) • Low tumor purity (% normal cells in tumor sample) • Requires different thresholds than germline variant calling when evaluating signal vs noise • Trade-off between sensitivity (ability to detect mutation) and specificity (rate of false positives)

39. MuTect

40. ICGC-TCGA DREAM Mutation Calling challenge • MuTect ranked highly in all 4 datasets in the DREAM challenges

41. 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

42. Example VCF

43. Workflow for Data Analysis Read Generation Read Mapping Variant Calling Annotation and Filtering - Genome Annotation Database - Criteria for filtering Driver Mutations

44. Annotation and Functional Prediction

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

46.  1000 Genomes Project • 15 million SNPs • 1 million short insertions/deletions   • 20,000 structural variants The 1000 Genomes Project Consortium, Nature 2010 (www.1000genomes.org)

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

48. COSMIC http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

49. Identifying causal variants: filtering Stitziel, Genom Biol 2011

50. Workflow for Data Analysis Read Generation Read Mapping Variant Calling Annotation and Filtering - Significantly mutated genes - Pathway and network analysis Driver Mutations