Next-generation sequencing and structural variation was presented. Structural variation refers to changes in chromosome structure, such as deletions, duplications, inversions, and translocations. Approaches for discovery include read pairs, read depth, split reads, and fine-mapping breakpoints through local assembly to identify signatures of structural variation. Read pairs and read depth were discussed in more detail, including mapping, filtering, clustering, and issues. Combining different approaches and algorithms is important as results can vary, and validation is needed.

1. Next-generation sequencing
and structural variation
Jan Aerts
Wellcome Trust Sanger Institute
jan.aerts@gmail.com

2. principles & pittfalls
vs
list of commands

3. What is structural variation?
• “variation that changes the structure of
a chromosome”
• Mechanisms: NAHR, NHEJ, FoSTeS
• This presentation: focus on discovery
(not: genotyping)
“experiment 4” from last slide Thomas

4. Types of structural variation

5. Approaches for discovery
Combination of:
• Read pairs
• Read depth
• Split reads
• Fine-mapping breakpoints: local assembly
=> Identify signatures

6. A. Read Pairs

7. RP - General principle
• Paired-end library => insert size
• Orientation/distance

8. RP - Signatures
Medvedev et al, 2009

9. RP - Real world

10. RP - Workflow overview
Mapping
 Identify discordant readpairs
 Cluster on location
 Filter on nr RPs/cluster
 Filter on RD
 Filter: mappingQ x #readpairs
 Identify signatures
 Alternative reference
 Validate

11. RP - Mapping
• Provides raw data => crucial
• MAQ/bwa
– only report one hit (mappingQ = 0)
– MAQ might prefer mismatches to aberrant
distance!
• Insert size = distribution instead of exact

12. RP - Discordant readpairs
• Orientation
• Distance
– Plot insert size distribution for chromosome
– Very long tail! => difficult to set cutoff:
• 4mad or 0.01%?

18. RP - Clustering
“standard clustering strategy”
– Only consider mate pairs that do not have
concordant mappings
– Ignore read pairs that have more than one
good mapping
Clustering: use insert size distribution
(e.g. 2x4 mad)

19. RP - Clustering: issues
• Ignores pairs that have >1 good mapping =>
no detection within repetitive regions
(segmental duplications)
• What cutoff for what is considered abnormal
distance? (4 mad? 0.01%? 2stdev?)
• Low library quality or mix of libraries =>
multiple peaks in size distribution

20. RP - Filtering
• On nr RPs/cluster
– Normally: n=2
– For high coverage (e.g. pilot 2: 80X): n=5
• On drop in RD & SR
• On (mappingQ x nrRP)
– If published data available: ROC for
different cutoffs mQxnrRP
– If not: very difficult

21. RP - Issues
• Difficult => different groups = different results
“consensus set”
– RP & SP: many set agree
– RD: totally different
• CEU (80X): sometimes drop in RD in all 3,
but RP spanning only in 2 => why??
• Mapper = critical; maq/bwa: only 1 mapping
(=> many false negatives); mosaik, mrFAST:
return more results

22. RP - Issues (2)
• Large insert size: low resolution for detecting
breakpoints
• Small insert size: low resolution for detecting
complex regions

23. B. Read Depth

24. RD - General principle
• Similar to aCGH: using reference RD
file (e.g. based on 1kG)
• In theory: higher resolution, but noisier
than aCGH
– Algorithms not mature yet
– More complex steps
=> Data binned

25. RD - Exome
here: using exome data

26. RD - Example

27. RD - Workflow overview
• Mapping
• Read filtering
• GC correction
• Spike identification
• Validation

29. RD - mapping
Critical…
(see RP)

30. RD - Filtering
• mapQ
– mapQ >= 0 (noisy; few FN, many FP)
– mapQ >= 10
– mapQ >= 30 (many FN, few FP)
• Mean depth exon (often: e.g. +/- 0.01)
– Mean depth > 1
– Mean depth > 5

31. RD - Filtering: what’s left
mapQ >= 0 mapQ >= 10 mapQ >= 30
all 207,000 207,000 207,000
mean DP exon > 1 169,000 163,000 162,000
mean DP exon > 5 160,000 153,000 152,000

32. RD - correction
• Mainly: GC
– Other: repeat-rich regions, mapping Q, …
• Fit linear model GC-content exon and
RD of exon
=> noise decreases

35. RD - segmentation
• Identify spikes
• Many segmentational algorithms, e.g.
GADA
• Issues: setting parameters: when to cut
off peaks?
– Combine outputs from different runs with
different parameters
– Compare to known CNVs

36. RD - Combine algorithms

39. RD - Issues
• How to assess TP/FP/FN? => compare
with known CNVs
• Breakpoints: unknown
– 1 datapoint/exon
– Can be outside of exon
• Different parameters for rare vs
common CNVs => which?

40. C. Split Reads

41. SR - Principle

42. SR - Mapping
Short subsequences => many possible
mappings
Solution: “anchored split mapping” (e.g.
Pindel)

44. D. Local reassembly
Aim: to determine breakpoints
Which reads?
– for deletions: local reads
– for insertions: hanging reads for read pairs with
only one read mapped
– (rather not: unmapped reads)
For large region: split up

45. Assemblers
Velvet
ABySS
TIGRA
…

47. Conclusions
• Available algorithms: more to
demonstrate technique rather than
complete solution
• Different algorithms => different results

48. Chris Yoon

50. Genotyping
• Create alternative reference => remap reads
– All reads vs reads covering variant locis
– Whole-genome vs concatenation of variant loci
• Homozygous insertions/deletions: should disappear
• Heterozygous insertions/deletions: should have different
signatures
• Bayesian approach: see what’s the most likely: do the reads
support wild-type/het/homnonref?
• Not exact mapping => local reassembly
– Microhomologies & non-template sequence => “breakpoint”
= region of 2-10 bp
• Convention: left-most position reported (but not always)

51. References and software
• Medvedev P et al. Nat Methods 6(11):S13-S20 (2009)
• Lee S et al. Bioinformatics 24:i59-i67 (2008)
• Hormozdiari F et al. Genome Res 19:1270-1278 (2009)
• Campbell P et al. Nat Genet 40:722-729 (2008)
• Ye K et al. Bioinformatics 25(21):2865-2871 (2009)
• Chen K et al. Genome Res 19:1527-1541 (2009)
• Yoon S et al. Genome Res 19:1586-1592 (2009)
• Du J et al. PLoS Comp Biol 5(7):e1000432 (2009)
• Aerts J & Tyler-Smith C. In: Encyclopedia of Life Sciences
(2009)

52. Questions?