Bioinformatic approaches for analysis of coral-associated bacteria using R programming language
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https://doi.org/10.15625/1811-4989/18/4/15320Keywords:
16S rRNA, Acropora tenuis, bioinformatics, coral-associated bacteria, R programming languageAbstract
Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a set of different bioinformatics tools to create our own workflow for microbial community analysis in a continuous and synchronous manner. For the first effort, we tried to investigate the composition and abundance of coral-associated bacteria using their 16S rRNA gene amplicon sequences. The workflow or framework includes the following steps: data processing, sequence clustering, taxonomic assignment, and data visualization. Moreover, we also like to catch readers’ attention to the information about bacterial communities living in the ocean as most marine microorganisms are unculturable, especially residing in coral reefs, namely, bacteria are associated with the coral Acropora tenuis in this case. The outcomes obtained in this study suggest that the DADA2 pipeline written in R programming language is one of the potential bioinformatics approaches in the context of microbiome analysis other than using various software. Besides, our modifications for the workflow execution help researchers to illustrate metagenomic data more easily and systematically, elucidate the composition, abundance, diversity, and relationship between microorganism communities as well as to develop other bioinformatic tools more effectively.
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