Yu Geng1,2, Zhongmeng Zhao2, Jianye Liu2. 1. School of Health Management, Jinzhou Medical University, Jinzhou 121001, China. 2. School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.
Abstract
OBJECTIVE: To reconstruct tumor clonal haplotypes based on the third-generation sequencing data to effectively identify tumor heterogeneity. METHODS: We developed an algorithm for extracting somatic mutational event from the mixed tumor data and determining the connection weight of each somatic cell mutation site through the probability function. A reconstruction algorithm of the haplotype was designed based on the maximum spanning tree, and following the principle of inheritance between tumor clones, the connection pattern was determined at each mutation site in the clonal maximum spanning tree in a stepwise manner. The number, ratio and evolution of the sub-clones were estimated using the depth stripping method. RESULTS: In the simulation experiments, we analyzed the accuracy of the algorithm based on 4 indexes, namely the coverage, read length, subclone number and somatic variant rate, and the Results demonstrated a good robustness of the algorithm. The Results of the experiments showed that the mean sub-clone haplotypes accuracy exceeded 97%, suggesting that this algorithm significantly outperformed the previous Methods. CONCLUSIONS: The proposed method can accurately reconstruct tumor subclonal haplotypes and clarify the process of tumor clonal evolution, and can thus provide a theoretical basis for tumor heterogeneity research and assist in clinical decision-making.
OBJECTIVE: To reconstruct tumor clonal haplotypes based on the third-generation sequencing data to effectively identify tumor heterogeneity. METHODS: We developed an algorithm for extracting somatic mutational event from the mixed tumor data and determining the connection weight of each somatic cell mutation site through the probability function. A reconstruction algorithm of the haplotype was designed based on the maximum spanning tree, and following the principle of inheritance between tumor clones, the connection pattern was determined at each mutation site in the clonal maximum spanning tree in a stepwise manner. The number, ratio and evolution of the sub-clones were estimated using the depth stripping method. RESULTS: In the simulation experiments, we analyzed the accuracy of the algorithm based on 4 indexes, namely the coverage, read length, subclone number and somatic variant rate, and the Results demonstrated a good robustness of the algorithm. The Results of the experiments showed that the mean sub-clone haplotypes accuracy exceeded 97%, suggesting that this algorithm significantly outperformed the previous Methods. CONCLUSIONS: The proposed method can accurately reconstruct tumor subclonal haplotypes and clarify the process of tumor clonal evolution, and can thus provide a theoretical basis for tumor heterogeneity research and assist in clinical decision-making.
Authors: Andrew Roth; Jiarui Ding; Ryan Morin; Anamaria Crisan; Gavin Ha; Ryan Giuliany; Ali Bashashati; Martin Hirst; Gulisa Turashvili; Arusha Oloumi; Marco A Marra; Samuel Aparicio; Sohrab P Shah Journal: Bioinformatics Date: 2012-01-27 Impact factor: 6.937
Authors: David Gordon; John Huddleston; Mark J P Chaisson; Christopher M Hill; Zev N Kronenberg; Katherine M Munson; Maika Malig; Archana Raja; Ian Fiddes; LaDeana W Hillier; Christopher Dunn; Carl Baker; Joel Armstrong; Mark Diekhans; Benedict Paten; Jay Shendure; Richard K Wilson; David Haussler; Chen-Shan Chin; Evan E Eichler Journal: Science Date: 2016-04-01 Impact factor: 47.728