Parliament2 is a multi-tool ensemble structural variant caller that achieves better recall and specificity than individual tools. It runs multiple SV detection tools in parallel, which allows it to understand how to weight and combine results from different tools to produce very high precision calls. The GIAB truth set is crucial for Parliament2, enabling it to assess tool accuracy, add quality values to calls, and implement filtering to achieve over 99% specificity for some SV categories.

1. Parliament2: SV with the
GIAB v0.6 Truth Set
Andrew Carroll, DNAnexus*
*Currently at Google

2. Structural Variants (SV) have different types and sizes
Detection is difficult in
short-reads which can’t
span an event.
Signal to detect different
classes come in different
forms as well:
Coverage
Split-read alignment
Pair orientation
Pair insert size
Soft-clipping

3. Parliament2 is a multi-tool ensemble SV caller

4. Why run Parliament2?
(Items in blue are only possible due to GIAB truth set)
Parliament2 is fast (3-5 hours for a whole genome on a 16-core machine)
Parliament2 achieves better recall across size classes
Parliament2 achieves very high specificity (>99% for certain categories)
Parliament2 provides independent confidence measures for each event

5. Parliament Parallelizes Certain non-Parallel Tools

6. Parliament Parallelizes Certain non-Parallel Tools

7. Additional callers improve recall

8. Knowing caller overlaps enables very high precision

9. Knowing caller overlaps enables call filtering

10. Which can allow for quality values based on GIAB

11. Accuracy benchmarks for short-read methods

12. Accuracy benchmarks for short-read methods

13. Accuracy benchmarks for short-read methods

14. Parliament2 resources

15. Final observations
The GIAB SV truth set is essential for our ability to run multiple callers and then
understand how we should weight and combine them.
It is also fundamental to the ability to add quality values and filters.
Parliament2 is less a new method and more a new way to understand, value, and
combine the tools that we have. There is an interesting property where even if you
have one method that always outperforms another, the combination of the two can
still be stronger than either alone.

16. Thanks
Olga Krasheninina
Fritz Sedlazeck
William Salerno
Goo Jun
Richard Gibbs
Eric Boerwinkle
Samantha Zarate