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MetaG: a graph-based metagenomic gene analysis for big DNA data

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Abstract

Microbial interactions and relationships are significant for animals, insects and plants. Metagenomic research enables properassessments and analysis for microbial organs and communities. The analysis helps to gain detailed insights on miscopies insects. Recent machine learning techniques focused on algorithms and data mining tools to check the depth of interactions and relationships on metagenomic dataset. Accurate analysis over large genes helps to solve real-world problems for public interest. In this regard, graph-centric big gene dataset representations are very important. De Bruijn graph is one the pivotal media to demonstrate the relationships and interactions of large genes dataset or metagenomic dataset. In this research, mapping-based metagenomic graphical (MetaG) genomes representation has been demonstrated. Data cleaning is done before applying graphical illustration. Random mapping is used to assess the variations in dataset. Euler path-based De Bruijn graph is used to sketch the gene annotation, translations, signaling and coding. This research helps in computational biology to map the genomic information in graphical ways with clear conceptions. Adequate experimental comparisons as well as analysis established the claims with tables and graphs.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh

    Linkon Chowdhury, Mohammad Ibrahim Khan & Kaushik Deb

  2. Department of Computer Science and Engineering, East West University, Aftab Nagar, Dhaka, Bangladesh

    Sarwar Kamal

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  1. Linkon Chowdhury
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Correspondence to Linkon Chowdhury.

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Chowdhury, L., Khan, M.I., Deb, K. et al. MetaG: a graph-based metagenomic gene analysis for big DNA data. Netw Model Anal Health Inform Bioinforma 5, 27 (2016). https://doi.org/10.1007/s13721-016-0132-7

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