1. Effect of Repeats on the
Characterization of Structural Variation
Nancy F. Hansen, Ph.D..
September 15, 2016
2. Outline of my talk
• Description of “PBRefine” callset
• Refinement of regions by alignment of PacBio assemblies to the human
reference (Build37) with nucmer (MUMmer3.23, Kurtz et al., Genome
Biology (2004))
• Characterization of SVs using mummerplot dot plots
• Role of repeats in curation of structural variation
• Ambiguities in the positions of insertions and deletions due to repeats
• “Correct” answer can be dependent on alignment algorithm
• Evidence from different technological platforms can point to different
breakpoints
3. The PBRefine Pipeline
Extract reference
sequence surrounding
variant predictions
from reference
Align reference
sequence to PB
assembly* with
MUMmer
Count end-
to-end
alignments
Discard
region as
repetitive
Align assembly region
back to reference with
MUMmer
Characterize
variants
More
than 2
2 or
fewer
* CA and hybrid Falcon
assemblies for all
three trio members
4. Why long read assemblies for structural variant prediction?
• Continuity
• Consensus accuracy
Why not long read assemblies?
• Often assemblers will miss the second haplotype for diploid
organisms
Accurate positions,
accurate consensus
for novel inserted
sequences
Inaccurate genotypes
for heterozygotes
labeled as
homozygotes
5. How often are variants confirmed?
1. Consider only SVs for which there are one or two contigs
found in the assembly
2. Require consistent position and variant type
Variant
Type
Total
Calls
Assembler Variants
confirmed
in HG002
Variants
confirmed
in HG003
Variants
confirmed
in HG004
Overall 6,784 Mt.
Sinai/Falcon
1,851
(27.3%)
1,729
(25.5%)
1,708
(25.2%)
NHGRI/CA 1,808
(26.7%)
1,565
(23.1%)
1,545
(22.8%)
Insertion
s
743 Mt.
Sinai/Falcon
171 (23.0%) 157 (21.1%) 156 (21.0%)
NHGRI/CA 155 (20.9%) 134 (18.0%) 130 (17.5%)
Deletions 6,041 Mt.
Sinai/Falcon
1,680
(27.8%)
1,572
(26.0%)
1,552
(25.7%)
NHGRI/CA 1,653 1,431 1,415
