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Published on: July 12, 2018
Automating Microbial Community Analysis (AMCA): Development and application of an amplicon based graphical pipeline
Nitika Rana1, Archana Angrup2, Karalanglin Tiewsoh1
1Department of Medical Microbiology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
Introduction:
Amplicon sequencing is a targeted approach used to assess the diversity of microbial communities by amplifying and sequencing a specific genetic locus from DNA. QIIME2 is one of the most prevalent methods for metagenomics analysis due to its plugin-based design wherein distinct modules can be utilized to perform specific functions. However, QIIME2 data input, and plugin utilization is cumbersome to navigate. Previous amplicon pipelines also lack host depletion and statistical biomarker identification modules from upstream and downstream analysis.
Methods:
To this effect, we assembled a simple and customizable Zenity based GUI workflow for analysing amplicon data with Automating Microbial Community Analysis (AMCA). The analysis integrates key attributes of amplicon analysis: host depletion with Bowtie2 and biomarker prediction by LEfSe. The bash-based analysis guides and allows the user to select filtering parameters based on intermediate results while minimizing the need to navigate command-based plugins.
Results:
The outputs from the AMCA workflow include the filtered and host-depleted raw sequencing data, taxonomic abundances, alpha and beta diversity indices, alpha rarefaction analysis, phylogenetic tree (rooted and unrooted) and significant features which explain key microbial differences between conditions/classes of the experiment. The implementation of the designed workflow has been tested on a pilot study based on amplicon sequencing in 100 samples from patients of Chronic Kidney Disease and healthy controls. The exploratory LEfSE analysis revealed key taxa Streptococcus, Bacteroides and Faecalibacterium to vary between disease and control conditions. The source code related to the analysis can be assessed from the Github repository at https://github.com/Nitika-Rana/AMCA.
Conclusion:
The study delivers an efficient, user-friendly, and customizable workflow for amplicon analysis, simplifying QIIME2 execution while enabling host depletion and biomarker characterization.
