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Network Modeling Analysis in Health Informatics and Bioinformatics

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01.12.2016 | Original Article

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

verfasst von: Linkon Chowdhury, Mohammad Ibrahim Khan, Kaushik Deb, Sarwar Kamal

Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics | Ausgabe 1/2016

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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.

Vorheriger Artikel Implementation and comparison of kernel-based learning methods to predict metabolic networks

Nächster Artikel Big data analytics in bioinformatics: architectures, techniques, tools and issues

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Titel: MetaG: a graph-based metagenomic gene analysis for big DNA data
verfasst von: Linkon Chowdhury
Mohammad Ibrahim Khan
Kaushik Deb
Sarwar Kamal
Publikationsdatum: 01.12.2016
Verlag: Springer Vienna
Erschienen in: Network Modeling Analysis in Health Informatics and Bioinformatics / Ausgabe 1/2016
Print ISSN: 2192-6662
Elektronische ISSN: 2192-6670
DOI: https://doi.org/10.1007/s13721-016-0132-7

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