The document describes a proposed approach to integrate variant calls from multiple methods and datasets to generate high-confidence SNP/indel and structural variant calls. The approach involves generating variant calls from multiple methods, comparing and integrating the calls, manually inspecting data to understand differences, generating integrated calls using several methods, and combining integrated calls with heuristics and machine learning to generate final high-confidence calls. Key steps include generating VCF and BED files from multiple methods by October/November 2015, adding difficult to map variants by December 2015, and generating final high-confidence calls by January 2016.

1. Integration to form high-confidence
SNP/indel calls
VCFs with 0 FP PASS and
0 FN PASS+filtered in
BED files
If 1+ datasets PASS and
all PASSing datasets have
same genotype
High-confidence variant,
include in high-
confidence regions
If all datasets are filtered
or outside BED
Unless manually inspect
alignments: not high-
confidence, exclude +-50
bp from high-confidence
regions
If PASSing datasets
disagree about genotype
or variant
Unless manually inspect
alignments: not high-
confidence, exclude +-50
bp from high-confidence
regions
If inside BED and not in
VCF for 1+ datasets, and
no datasets have PASSing
variants
High-confidence region

2. Timeline to form high-confidence
SNP/indel (and Hom Ref) calls with local
phasing
Generate vcf and
bed files from
multiple methods
Generate
integrated calls
October 15, 2015
Dec 1, 2015
Add difficult-to-map
variants from
10X/moleculo?

3. svclassify: Methods to classify
candidate SVs as likely true positives

4. Reference genome,
Repeatmasker data
SVClassify
Up to 180
annotations
per SV
Aligned sequence
data (BAM file)
List of structural
variants (bed file)
Up to 35
selected
annotations
per SV
One class
methods
Unsupervised
clustering
Support
vector
machine
L1
distance
Svclassify: SV Integration Method

5. One-class scores for Random Non-SVs
and “Validated” Callsets
http://biorxiv.org/content/early/2015/05/22/019372
2676
68

6. svclassify
Strengths
• Can use bam file from any
technology
• Combines information
across technologies
• Might work for many types
of SVs
• Potentially could classify
type of SV
Weaknesses
• Works best for candidates
with precise breakpoints
• Doesn’t refine breakpoints
• Limited efficacy for
deletions <100bp

7. Proposed approach to form high-
confidence SV (and non-SV) calls
Generate candidate calls from multiple
methods
Compare and integrate candidate calls in
different ways and manually inspect data
to understand differences; refine calls?
Generate integrated calls with several
methods (MetaSV, Parliament, svclassify,
others?)
Combine integrated calls (with heuristics
and/or machine learning) to generate
high-confidence calls
https://docs.google.com/spreadsheets/d/1Pj4nSzH742g40wJz2fA6f8kFtZYAToZpSZYVPiC5st4/edit?usp=sharing
August 30, 2015
Nov 1, 2015
Dec 1, 2015
Jan 26, 2016

8. Phasing Integration
Methods
• Trio-based
• Read clouds
– CG LFR
– 10X
– Moleculo
• Read-backed
– PacBio (unzipping assembly)
• Population-based?
Integration approaches
• Comparison methods?
• Integration methods?
– How to decide who is right?
• Can we use these to
evaluate variant call
accuracy similar to pedigree
phasing?
• How to represent phasing
and uncertainty?
• Who might do this?

9. Discussion points
Missing analyses
• Phasing
comparison/integration
• Using ALT loci
• Building and using
“personalized reference”
• Integrate the SV integration
methods?
Outstanding questions
• How to integrate “difficult”
variants, esp. from 1 technology?
– SNP/indels in homologous regions
– Copy number of Seg Dups
– Long tandem repeats
• How do we assess the accuracy of
our calls?
– Manual curation (svviz, loupe, igv,
GeT-RM browser, …)
• Crowdsource this?
– Experimental confirmation
• What format should we use for
candidate SVs? For our high-
confidence calls?
• When will we use GRCh38?
• Additional coordination needed?