1. Reconstructing the Evolutionary History of Complex Human Gene Clusters
Y. Zhang, G. Song, T. Vinar,
E. D. Green, A. Siepel, W. Miller
RECOMB 2008
Speaker: Fabio Vandin

2. Outline Motivation Problem definition
Simple algorithm for simple model
SIS algorithm for complex model
Results on human genome
Evaluation of SIS algorithm

3. Gene cluster Group of related genes Probably formed by duplications:
followed by functional diversification
with deletions, cause human genetic diseases

4. “History” of a gene cluster?
80%
85%
93%
89%
98%
Percentage of similarity in self-alignment in the human genome

5. Dot-plots of self-alignments
Human UGT2 cluster

6. Data Preparation Atomic segments: Collapsible segments:
Self-alignment (forward and reverse-complement): pairs A ≈ B
Transitive closure property:
A ≈ B, B ≈ C A ≈ C
Maximize each alignment
Collapsible segments:

7. Computational Problem
Input: sequence of atomic (non-collapsible) segments
Output: (most probable) sequence of events (or the number of events) such that if we unwind these events in the input sequence, we obtain a sequence containing only a single atomic segment

8. Simple Model Assumptions:
Only duplications (possibly with reversal and tandem duplications)
No duplication inside the originating region
The most important:

9. Simple Model (2) Given assumptions 1 e 2, each event is
of “identical” (reversed) regions of consecutive atomic segments ( copied to )
Problem statement

10. Simple Model (3)
Candidate definition
Is this definition reasonable?

11. Simple Model (4)
Algorithm
Analysys

12. Simple Model: limitations
Assumptions:
“large scale deletions are likely to occur”
“atomic boundary reuses violating assumption are not uncommon”
Same number of events, but multiple way of reconstructing the history
NB: not solved with SIS, but you can assign probability..

13. Stochastic Model Event : History: Target distribution of histories:
Duplication (possibly with reversal)
Deletion (with restrictions)
History:
Target distribution of histories:
is the number of reused atomic boundaries and

14. Algorithm for Stochastic Model
Sample histories from the target distribution and compute the mean value (of the function we are interested in):
sample from
given , estimate
e.g., gives the number of events
Problem: how to sample from the target distribution?

15. Sequential Importance Sampling (SIS)
Goal: compute
Target distribution:
Trial distribution:
Sample’s weight:
Output:

16. SIS for gene cluster history
Duplication

17. SIS for gene cluster history (2)
Deletion
only without atomic boundary reuse
only if “the atomic segment pair flanking a deletion site appears elsewhere” in the seq.

18. Application to Human Gene Clusters
human genome assembly hg18 self alignment: 457 duplicated regions
alignments: at least 500 bp, >= 70% identity
segments separated by no more than 500 Kbp
only long and non-trivial regions considered
165 biomedically interesting clusters (~111 Mbp)
5 divergence thresholds: GA, OWM, NWM, LG, DOG
825 combinations of gene cluster and divergence threshold

19. Application to Human Gene Clusters (2)

20. Application to Human Gene Clusters (3)

21. Application to Human Gene Clusters (4)
“… help prioritize the selection of notably interesting gene clusters for more detailed comparative genomics studies”
“… to compare cluster dynamics in certain lineages to observed phenotypic differences among primates”
“… such sequence data should reveal differences among primate species of possible relevance for selecting species for further biomedical studies”

22. Evaluation of the SIS Algorithm
Estimate parameters from the human genome for the events (e.g., duplications):
39% of duplication “reversed”, deletions=2% of duplications
Starting from 500 Kbp sequence, generate 10 genome clusters for N= 10,20,…,100 events

23. Evaluation of the SIS Algorithm (2)

24. Discussion Main Limitations: Future Directions:
details of data preparation
choice of the parameters/distributions
evaluation of the SIS algorithm
Future Directions:
include other types of events
understand the stochastic model
how to evaluate a model?
how to evaluate an algorithm?