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

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

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

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

Assembly Overview Assembly Scaffolding

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

Statistical Error detection Significant deviations from average coverage.

Distribution of clone length

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 

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

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

ROC Curve

Simulated Data

Drosophila Assembly

Cross Species

Questions?