Identification of transposon insertion polymorphisms by computational comparative analysis of next generation personal genome data

Structural variations (SVs) in a genome are now known as a prominent and important type of genetic variation. Among all types of SVs, the identification of transposon insertion polymorphisms (TIPs) is more challenging due to the highly repetitive nature of transposon sequences. We developed a computational method, TIP-finder, to identify TIPs through analysis of next generation personal genome data and their extremely large copy numbers. We tested the efficiency of TIPfinder with simulated data and are able to detect about 88% of TIPs with precision of ≥91%. Using TIP-finder to analyze the Solexa pair-end sequence data at deep coverage for six genomes representing two trio families, we identified a total of 5569 TIPs, consisting of 4881, 456, 91, and 141 insertions from Alu, L1, SVA and HERV, respectively, representing the most comprehensive analysis of such type of genetic variation.