1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Mapping and Genome Rearrangement
Module 3
Mapping and Genome Rearrangement
Jared Simpson, Ph.D.
Bioinformatics for Cancer Genomics
May 30 – June 3, 2016
from: doi: /nmeth.2258

4. Learning Objectives of Module
Understand mapping reads to a reference genome
Understand the FASTQ and SAM/BAM file formats
Learn common terminology used to describe alignments
Learn how to find genome rearrangements using read pairs
Run a mapper and rearrangement caller

5. Sequencing platforms $ Increasing Data Per Run $ Increasing Run Time
14TB/run $
600Gb/10d
100Gb/15d
120Gb/1d
90Gb/10d
Increasing
Data
Per Run
150Mb/3h
2Gb/27h
700Mb/23h
100Mb/1h $
Increasing Run Time

6. Illumina Sequencing

7. Basecalling
Prediction of the DNA sequence from the images

8. Sources of error Illumina: Pre-phasing & Phasing

9. Error Profiles Illumina 454/Ion Torrent Pacbio and Oxford Nanopore
Low error rate (~0.5%), mainly substitutions
454/Ion Torrent
Mainly insertions/deletions in homopolymer runs
Pacbio and Oxford Nanopore
Single molecule sequencers
Higher error rate, mixture of insertions, deletions, substitutions

10. Illumina Error Profile

11. What is a FASTQ file?

12. What is a FASTQ file?
Read name

13. What is a FASTQ file?
Basecalled sequence

14. What is a FASTQ file?
Quality separator

15. What is a FASTQ file?
Base quality scores

16. What is a base quality score?
Phred quality scores:
Estimate of probability the base call is incorrect
Base Quality
Perror(obs. base) 3
50 % 5
32 % 10
10 % 20
1 % 30
0.1 % 40
0.01 %

17. Reference Mapping

18. Reference Mapping Why do we map reads to the reference?
By comparing the reads from a sequenced individual to a reference genome we can identify variants like SNPs, and rearrangements
To do this we need to identify where in the reference genome that a read might have come from

19. Reference Mapping Issues
The genome is very large and repetitive
The mapping program must be efficient and tolerant of repetitive sequences
Mappers like BWA using an index of the reference genome to rapidly identify possible mapping locations

20. Reference Mapping Issues
The reads contain sequencing errors
The mapping program must tolerate differences between the reads and the reference
Typically the mapper will find exact-match seeds then refine the seed alignments using dynamic programming
Mapping reads with many errors or insertions/deletions is much harder

21. Reference Mapping Issues
Short read sequences produce huge amounts of data
The mapping algorithm must be extremely efficient while accounting for the issues discussed above

22. Choosing a Mapper Needs to be accurate Needs to be sensitive
Misaligned reads are a source of false positive variant calls
Needs to be sensitive
Must allow for differences between the individual and reference
Needs to be fast

24. Reference Mapping
Reference genome
Sequence read ?

25. Reference Mapping
Reference genome x x x
Sequence read

26. Mapping Quality
Phred-scaled estimate of the probability that the chosen mapping is wrong
1 in 1000 reads with “Q30” alignment will be placed incorrectly
What causes mapping errors?
High error rate
Repetitive sequence
Differences between the reference and sequenced sample

27. What are Paired Reads? DNA fragment ATCAAGA CTACATG Insert size (IS)
Slides by M. Brudno 27

28. Paired Reads
Reference genome ?
Sequence read pair 28

29. Paired Mapping
Reference genome x x
Sequence read pair

30. Paired Mapping
Reference genome x x x x x x x x
Sequence read pair

31. Sequence Alignment/Map Format
SAM/BAM is a format for working with mapped reads
SAM is tab-delimited text representation
BAM is a compressed binary representation
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT

32. SAM Format Flag indicates the reference strand, pairing information
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT
Read ID
Flag
Flag indicates the reference strand, pairing information

33. SAM Description
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT
Chromosome
Position

34. SAM Description
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT
Mapping Quality

35. SAM Description Ref ACGATACATAC Ref GACA-AACC
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT
CIGAR
Ref ACGATACATAC Ref GACA-AACC
Read ACGA-ACATAC Read GTCATAACC
CIGAR: 4M1D6M CIGAR: 4M1I4M

36. Mate chromosome, position
SAM Description
SRR M = NCCAGCAGCCATAACTGGAATGGGAAATAAACACTATGTTCAAAGCAGAGAAAATAGGAGTGTGCAATAGACTTAT
Mate chromosome, position
Insert size
ATCAA
CTAAG
Insert size (IS)

37. Resources samtools: toolkit for working with SAM/BAM files
Convert between SAM/BAM
Sort alignments
Extract alignments for a given genomic location
SAM/BAM specification:
Questions/Help

38. Viewing Alignments - IGV

39. Alignment Problems

40. Alignment Problems

41. We are now going to start a read mapping exercise

42. We are on a Coffee Break & Networking Session

43. Types of variation Single Nucleotide Variants (SNVs)
Insertions/deletions (INDELs)
Structural variations
Large insertions and deletions
Inversions
Translocations
Copy number variation

44. Structural variants using paired-end reads
Genomic DNA
Fragmentation and size selection ( bp)
Add sequencing adaptors
Sequence both ends

45. Read pair orientation Reference read pair Expected orientation:
one read on the forward strand, one read on the reverse strand

46. Fragment size distribution
from: doi: /ng.3121
Fragment/insert size is determined by library preparation
Pairs that match the expected orientation and distance are called concordant
Discordant read pairs give evidence of structural variation

47. SV Signatures: Deletion
sample
reference
Slides by M. Brudno

48. SV Signatures: Deletion
sample
reference
Signature: mapped insert size larger than expected
Slides by M. Brudno

49. SV Signatures: Insertion
sample
reference
Signature: mapped insert size smaller than expected
Slides by M. Brudno

50. SV Signatures: Tandem Duplication
sample
reference
Signature: wrong orientation

51. SV Signatures: Inversion
sample
reference
Signature: wrong orientation

52. SV summary Type Mapped Distance Orientation Insertion too small
correct
Deletion
too big
Inversion *
Tandem duplication
Interchromosomal
different chromosomes
N/A
Slides by M. Brudno

53. Problems: missed large insertion
sample
reference
Insertions larger than insert size cannot
be detected this way

54. Deletion: split read signature
don
ref
Signature: read aligns in two pieces, one on either side of the breakpoint

55. Gene fusions
if a linking signature connects two genes, this might indicate a gene fusion
Gene X
ChrA
Gene Y
ChrB
Gene XY Protein

56. Somatic vs. Germline
When sequencing cancers we want to know about the somatic changes – the mutations that are only in the tumour
We do this by looking for evidence of structural variation that is only in the tumour sample but not in the normal sample

57. We are now going to start an exercise in structural variant detection

58. We are on a Coffee Break & Networking Session

59. Any questions? jared.simpson@oicr.on.ca