1. Jeong-Hyeon Choi, Sun Kim, Haixu Tang, Justen Andrews, Don G. Gilbert
A machine-learning approach to combined evidence validation of genome assemblies 
Jeong-Hyeon Choi, Sun Kim, Haixu Tang, Justen Andrews, Don G. Gilbert
John K. Colbourne
Presented By – F A Rezaur Rahman Chowdhury

2. Mate-Pair Shotgun DNA Sequencing
DNA target sample
SHEAR & SIZE
End Reads / Mate Pairs
550bp
CLONE & END SEQUENCE
10,000bp

3. Assembling the fragments
NOte that contig orientation/order is not determined

4. Building Scaffolds Break DNA into random fragments
Sequence the ends of the fragments
Assemble the sequenced ends
Build scaffolds
We need to determine the relative order/orientation of contigs
Using forward-reverse constraints helps

5. Assembly Overview
Assembly
Scaffolding

6. Assembly Algorithms Greedy (TIGR , phrap, CAP3) De bruijn Graph
Graph based Greedy(Celera, Arachne)
Euler path based

8. Statistical Error detection
Significant deviations from average coverage.

9. Distribution of clone length

10. Good and Bad Clones
intra-contig or intra-scaffold clone is called good if the absolute Z- score of its length is smaller than a threshold 
Half-placed clones also bad
Clones with paired-end reads that are placed in the same or outer orientation 

11. Machine-learning approach
Combine evidence assembly validation
Features are taken from the statistical approaches
Five different classifier (J48, RF, RT, NB, BN)

12. Evaluation
Simulated dataset with different error rates ( 0.001, 0.003, 0.005)
Draft Assembly of Drosophila (D. mojavensis, D. erecta and D. virilis)

13. ROC Curve

14. Simulated Data

15. Drosophila Assembly

16. Cross Species

17. Questions?