A New Algorithm for Identifying Genome Rearrangements in the Mammalian Evolution.

Juan Wang,Maozu Guo,Yulan Zhao,Bo Cui

doi:10.3389/fgene.2019.01020

Abstract

Genome rearrangements are the evolutionary events on level of genomes. It is a global view on evolution research of species to analyze the genome rearrangements. We introduce a new method called RGRPT (recovering the genome rearrangements based on phylogenetic tree) used to identify the genome rearrangements. We test the RGRPT using simulated data. The results of experiments show that RGRPT have high sensitivity and specificity compared with other tools when to predict rearrangement events. We use RGRPT to predict the rearrangement events of six mammalian genomes (human, chimpanzee, rhesus macaque, mouse, rat, and dog). RGRPT has recognized a total of 1,157 rearrangement events for them at 10 kb resolution, including 858 reversals, 16 translocations, 249 transpositions, and 34 fusions/fissions. And RGRPT has recognized 475 rearrangement events for them at 50 kb resolution, including 332 reversals, 13 translocations, 94 transpositions, and 36 fusions/fissions. The code source of RGRPT is available from https://github.com/wangjuanimu/data-of-genome-rearrangement.

Highlights

The rapid development of sequencing technologies makes the phylogenetic analysis from the level of whole genome possible
All of the experiments were performed on a computer with Intel Vostro 14 2.0 GHz CPU, 4 GB RAM, and 500 GB Hard Disk Drives (HDD)
This paper proposes a new method, RGRPT, to infer ancestor rearrangement events

Summary

Introduction

The rapid development of sequencing technologies makes the phylogenetic analysis from the level of whole genome possible. The genome rearrangements of species are changes of syntenic block orderings and losing of sequence blocks. These events include reversal, translocation, transposition, fusion, fission, and so on (Xu et al, 2017; Cheng et al, 2019; Dong et al, 2018). In 1996, Hannenhalli designed an algorithm with O(n3) time complexity to compute it by Identification of Genome Rearrangements considering translocation events (Hannenhalli, 1995). The DCJ distance is introduced by Yancopoulos et al (Sophia et al, 2005), which uses the double cut and join (DCJ for short) operation to model rearrangement events, such as reversal, translocation, transposition, fusion, and fission in an unified way.

Methods

Results

Conclusion