Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

Zhaojuan Zhang,Wanliang Wang,Jijun Tang,Jiandong Wang,Ruofan Xia,Gaofeng Pan

doi:10.1186/s12859-020-03833-7

Abstract

BackgroundReconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data.ResultsIn response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median.ConclusionsOur experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

Highlights

Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction
Experimental environments and parameters setting In order to evaluate the performance of improved discrete quantum-behaved particle swarm optimization algorithm (QPSO) algorithm (IDQPSO)-Median for phylogenetic reconstruction, we conducted extensive experiments in a variety of datasets using a spectrum of parameter settings therein
The improvements are relatively small, it does demonstrate that population size has some effect on the performance of our median solver. These results show that the performance of the IDQPSO algorithm in obtaining the IDQPSO-Median requires additional time to converge in the context of larger populations comprising the search space, and incurs the attendant additional time cost as the population increases in size

Summary

Introduction

Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. As the smallest unrooted phylogenetic tree is defined by three leaves, one of the most significant problems in genome rearrangement analysis is termed as the Median Problem, which is defined as follows: given three input leaf genomes, find the genome (median) that minimizes the sum of evolutionary distances between the median and the three input genomes. Despite that the Median Problem is NP-hard for most criteria [6, 7], finding a solution for the median problem is essential since the recovered median is considered to be a good option for the ancestral genome but can further be used in a multitude of purposes in phylogenetic tree reconstructions

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Discussion

Conclusion