Genome in a Bottle Consortium: Bioinformatics Working Groups

September 2016 Workshop

Bioinformatics Working Groups

Thursday 15

th

 September 2016 9:00AM - 3:30PM

OVERVIEW

Current high-confidence calls include SNPs and small indels for ~90% of the genome, but

exclude the most difficult variants and regions. The goal of these data jamborees is to make

progress characterizing more difficult small variants and structural variants

SMALL VARIANTS (9:00-11:00 AM)

Motivation

● Current high confidence small variant calls and regions exclude many difficult variants,

so that these cannot be benchmarked.

● Also, only limited local phasing information is provided.

Schedule outline

● Update on current GIAB high-confidence calls, their improvements, and their limitations

(Justin Zook, NIST)

● Use of pedigree calls to improve reference material ground truth calls of NA12878. -

now include parents in pedigree analysis and new 300x NA12878 data.

(Sean Irvine, RTG)

● Incorporating new calls into Platinum Genomes by kmer analysis; how to incorporate in

proper ploidy of small variant calls inside CNVs (Mike Eberle, Illumina)

● Use of linked-read technology data to produce calls in the “dark” regions of the

genome and confirmation with long reads (Haynes Heaton, 10X).

○ Singleton vs Trio calling with RTG on 10X (Sean Irvine, RTG)

● Improving automated merging of call sets: variant representation canonization methods

(Sean Irvine, RTG)

○ Use vcfeval to build up set of minimal alleles from multiple callsets; then go back

and encode individual callsets using these alleles

● Transferring phasing from one call set to another to form high-confidence phased calls.

(Sean Irvine, RTG)
Unaddressed needs/questions

● How leverage and make calls on ALT loci, especially on GRCh38?

● Can we incorporate calls in difficult regions supported by only one technology?

● Should indels 20-100bp fall under our small variant integration methods or SVs?

STRUCTURAL VARIANTS (11:00AM-3:30PM)

Motivation for discussion

● Structural variants (e.g., indels>20-50bp, inversions, and complex changes) are not

represented in current GIAB high confidence calls

● Methods to integrate SV calls and form high-confidence calls and regions need to be

developed

Schedule outline

● 11:00 - 11:30: Overview (Justin Zook)

● 12:00 - 1:00: Callsets/Technologies

- Speakers (moderated by Ali Bashir):

- William Salerno - Baylor

- Jason Chin - Pacbio

- Alex Hastie - BioNano

- John Oliver - Nabsys

- Sofia Kyriazopoulou-Panagiotopoulou - 10X

- Michael Eberle - Illumina

● 1:30 - 3:30: Visualization/Integration

- Speakers (moderated by Justin Zook):

- Andrew Carroll - DNANexus

- Aaron Wenger - PacBio (Visualization)

- Nancy Hansen - NIH (Complex Events)

- Peyton Greenside - Verily (Crowdsourced manual curation)

- Ali Bashir - MSSM (Hybrid Calling)

- Integration Discussion (Led by Ali Bashir)

Unaddressed needs/questions

● What criteria to use to form high-confidence calls?

○ How to deal with uncertainty in predicted breakpoints, type, size, etc.?

● How to form high-confidence regions?

● How best to type SVs found in one technology in other technologies?
○ How to deal with uncertainty in breakpoints or exact sequence?

Notes from Discussion:

● Typing options:

● Parliament - illumina assembly and PacBio

● Svviz - map reads to REF and ALT

● Nabsys - map nanodetector reads to ref with and without variant

● Find kmers unique to event for typing

● LUMPY svtyper - new version under development

● Can with add a confidence interval to our high confidence calls? Or bin calls into

different precisions

● Tier 0: 2 technologies assembled both haplotypes across the breakpoint and they

agree

○ If discovery uses 2 technologies, then need a third

● Tier 1: 2 technologies assembled across the breakpoint and they agree

● Tier 2: One technology constructs sequence and other technologies support it

● Make these criteria tags on events. ALso tag with which callers and technologies

support the event

● Put merged calls in analysis folder

● Recruit classes for manual curation?

● Find regions where no one makes a call

Insertions

● Can we call an insertion high confidence without knowing the exact sequence?

○ It’s possible these could be complex

Form 2 SV integration teams:

● Team 1: Develop methods to compare sequence resolved structural variation from

different methods

○ Uses outputs of each method (Team 2 will focus on going back to the data)
○ Only use breakpoint-resolved methods (global or local assembly-based, or

split-read-based for simple deletions)

■ Any method that produces exact sequence (VCF with REF and ALT

sequence)

○ Perform multiple sequence alignment between haplotypes from different

methods to identify “concordant” assemblies

■ First find exact matches

■ Then move to more complex cases with inexact matches

■ Also later examine support for both haplotypes

■ How should we define “concordant”?

● Team 2: Apply “SV corroboration” methods to determine if each dataset supports calls

found by other datasets, and identify visualization methods for difficult sites

○ Easiest to interpret results from accurate break points (for simple deletions) or

assembled sequence (for any type)

○ Possible inputs

■ Just develop strawman set of rules

■ Output of team 1

■ All calls generated by breakpoint resolved methods

■ All calls from every method

■ Output of Justin's deletion integration

■ Develop a method to select non-overlapping inclusive set of calls from all

calls.

■ Input a set of false hypotheses to determine sensitivity and specificity for

each caller.

● Random locations

● Sites where parent is hom ref and child and other parent is variant

○ Possible outputs from each validating caller

■ Output confidence score for each call.

■ Estimate error of of breakpoint or size?

■ Often can infer genotype based on support for REF and ALT haplotypes

○ Existing methods within GIAB for typing include:

■ Parliament (Illumina/PacBio-assembly and PacBio-read-based)

■ svviz - map reads to REF or ALT from Illumina paired end or mate-pair,

PacBIo, 10X haplotype-separated

■ Nabsys - align mapping data to reference with and without SV

■ Bionano

■ Look for kmers unique to SV (Illumina working on methods to do this in

population data)

○ Other potential typing methods to explore
■ LUMPY svtyper

■ Spiral “graph genome database”

■ Seven Bridges graph genome aligner/variant caller

○ Other methods to generate features from data

■ Svclassify

■ Personalis - https://github.com/personalis/cnvthresher

○ How to handle nearby candidate SVs? Try different phase combinations? Or try

to phase using assembly?

○ Use features generated by all of these tools in a machine learning model to

classify sites

○ Visualization approaches

■ IGV - new extensions for PacBio and 10X in dev version

■ Dot plots - particularly helpful in repeats and complex SVs

■ PacBio GenomeRibbon.com

■ Verily’s CrowdVariant - could potentially present visualizations from any

of the above tools

○ Who to invite to help with the work?