Smash plus plus : an alignment-free and memory-efficient tool to find genomic rearrangements

被引:16
|
作者
Hosseini, Morteza [1 ]
Pratas, Diogo [1 ,2 ]
Morgenstern, Burkhard [3 ,4 ]
Pinho, Armando J. [1 ]
机构
[1] Univ Aveiro, IEETA DETI, Campus Univ Santiago, P-3810193 Aveiro, Portugal
[2] Univ Helsinki, Dept Virol, Haartmaninkatu 3, Helsinki 00014, Finland
[3] Univ Gottingen, Dept Bioinformat, Goldschmidtstr 1, D-37077 Gottingen, Germany
[4] Gottingen Ctr Mol Biosci GZMB, Justus von Liebig Weg 11, D-37077 Gottingen, Germany
来源
GIGASCIENCE | 2020年 / 9卷 / 05期
关键词
genomic rearrangement; alignment-free; genome comparison; genome duplication; data compression; information theory; probabilistic-algorithmic model; complexity; visualization; high-throughput sequencing; LARGE NUMBERS; COMPRESSION; SEQUENCE;
D O I
10.1093/gigascience/giaa048
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: The development of high-throughput sequencing technologies and, as its result, the production of huge volumes of genomic data, has accelerated biological and medical research and discovery. Study on genomic rearrangements is crucial owing to their role in chromosomal evolution, genetic disorders, and cancer. Results: We present Smash++, an alignment-free and memory-efficient tool to find and visualize small- and large-scale genomic rearrangements between 2 DNA sequences. This computational solution extracts information contents of the 2 sequences, exploiting a data compression technique to find rearrangements. We also present Smash++ visualizer, a tool that allows the visualization of the detected rearrangements along with their self- and relative complexity, by generating an SVG (Scalable Vector Graphics) image. Conclusions: Tested on several synthetic and real DNA sequences from bacteria, fungi, Aves, and Mammalia, the proposed tool was able to accurately find genomic rearrangements. The detected regions were in accordance with previous studies, which took alignment-based approaches or performed FISH (fluorescence in situ hybridization) analysis. The maximum peak memory usage among all experiments was similar to 1 GB, which makes Smash++ feasible to run on present-day standard computers.
引用
收藏
页数:15
相关论文
共 11 条
  • [1] An alignment-free method to find and visualise rearrangements between pairs of DNA sequences
    Pratas, Diogo
    Silva, Raquel M.
    Pinho, Armando J.
    Ferreira, Paulo J. S. G.
    SCIENTIFIC REPORTS, 2015, 5
  • [2] An alignment-free method to find and visualise rearrangements between pairs of DNA sequences
    Diogo Pratas
    Raquel M. Silva
    Armando J. Pinho
    Paulo J.S.G. Ferreira
    Scientific Reports, 5
  • [3] StreamNet plus plus : Memory-Efficient Streaming TinyML Model Compilation on Microcontrollers
    Hsu, Chen-fong
    Zheng, Hong-sheng
    Liu, Yu-yuan
    Yeh, Tsung tai
    ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 2025, 24 (02)
  • [4] Techniques for Memory-Efficient Model Checking of C and C plus plus Code
    Rockai, Petr
    Still, Vladimir
    Barnat, Jiri
    SOFTWARE ENGINEERING AND FORMAL METHODS, 2015, 9276 : 268 - 282
  • [5] MESH: A Memory-Efficient Safe Heap for C/C plus
    Vintila, Emanuel Q.
    Zieris, Philipp
    Horsch, Julian
    ARES 2021: 16TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, 2021,
  • [6] DNA sequence plus shape kernel enables alignment-free modeling of transcription factor binding
    Ma, Wenxiu
    Yang, Lin
    Rohs, Remo
    Noble, William Stafford
    BIOINFORMATICS, 2017, 33 (19) : 3003 - 3010
  • [7] SALT: a fast, memory-efficient and SNP-aware short read alignment tool
    Quan, Wei
    Liu, Bo
    Wang, Yadong
    2019 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2019, : 1774 - 1779
  • [8] RemixFormer plus plus : A Multi-Modal Transformer Model for Precision Skin Tumor Differential Diagnosis With Memory-Efficient Attention
    Xu, Jing
    Huang, Kai
    Zhong, Lianzhen
    Gao, Yuan
    Sun, Kai
    Liu, Wei
    Zhou, Yanjie
    Guo, Wenchao
    Guo, Yuan
    Zou, Yuanqiang
    Duan, Yuping
    Lu, Le
    Wang, Yu
    Chen, Xiang
    Zhao, Shuang
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2025, 44 (01) : 320 - 337
  • [9] Smart-DNN plus : A Memory-efficient Neural Networks Compression Framework for the Model Inference
    Wu, Donglei
    Yang, Weihao
    Zou, Xiangyu
    Xia, Wen
    Li, Shiyi
    Hu, Zhenbo
    Zhang, Weizhe
    Fang, Binxing
    ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION, 2023, 20 (04)
  • [10] TreeWave: command line tool for alignment-free phylogeny reconstruction based on graphical representation of DNA sequences and genomic signal processing
    Boumajdi, Nasma
    Bendani, Houda
    Belyamani, Lahcen
    Ibrahimi, Azeddine
    BMC BIOINFORMATICS, 2024, 25 (01):