Mako A graph-based pattern growth approach to detect complex structural variants

Introduction

Mako is designed for SV breakpoints detection based on a breakpoint graph. In particular, Mako first builds breakpoint graph from abnormal alignments, where these alignments can be derived from short-read and long-read (not tested). Afterwards, Mako uses pattern growth to detect the maximal subgraph structure as SVs. Each detected event is given a CXS score, indicating the complexity of detected events. Notably, Mako resolves the potential internal breakpoint connections of complex SV with the detected subgraph structure. For CSV breakpoints detection, Mako is comparable to assembly-based methods and outperformed other signature model-based methods.

Publications

  1. Mako: a graph-based pattern growth approach to detect complex structural variants
    Jiadong Lin, Xiaofei Yang, Walter Kosters, Tun Xu, Yanyan Jia, Songbo Wang, Qihui Zhu, Mallory Ryan, Li Guo, Chengsheng Zhang, Charles Lee, Scott E. Devine, Evan E. Eichler, Kai Ye, , 2021/3/2 -

Credits

  1. Jiadong Lin jiadong66@stu.xjtu.edu.cn
    Developer

    Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China

  2. KAI YE kaiye@xjtu.edu.cn
    Investigator

    Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China

  3. Xiaofei Yang xfyang@xjtu.edu.cn
    Contributor

    Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China

  4. Li Guo li_guo@xjtu.edu.cn
    Contributor

    Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China

Community Ratings

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pen***a@stu.xjtu.edu.cn (May 31, 2021)
Summary
AccessionBT007168
Tool TypeToolkit
CategoryStructural variant detection
PlatformsLinux/Unix, MAC OS X
TechnologiesJava
User InterfaceTerminal Command Line
Input DataBAM, FASTA
Download Count0
Country/RegionChina
Submitted ByPENG JIA
Fundings

2018YFC0910400