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
Credits
- Jiadong Lin jiadong66@stu.xjtu.edu.cn Developer
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China
- KAI YE kaiye@xjtu.edu.cn Investigator
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China
- Xiaofei Yang xfyang@xjtu.edu.cn Contributor
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China
- Li Guo li_guo@xjtu.edu.cn Contributor
Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, China
Community Ratings
| Usability | Efficiency | Reliability | Rated By |
|---|---|---|---|
| 2 users | |||
| Sign in to rate | |||
| pen***a@stu.xjtu.edu.cn (May 31, 2021) | |||
| wan***u@xjtu.edu.cn (August 19, 2021) | |||
| Accession | BT007168 |
|---|---|
| Tool Type | Toolkit |
| Category | Structural variant detection |
| Platforms | Linux/Unix, MAC OS X |
| Technologies | Java |
| User Interface | Terminal Command Line |
| Input Data | BAM, FASTA |
| Download Count | 0 |
| Country/Region | China |
| Submitted By | PENG JIA |
2018YFC0910400