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. 2020 Dec 2;15(12):e0243241.
doi: 10.1371/journal.pone.0243241. eCollection 2020.

CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis

Sebastian Hupfauf  1 , Mohammad Etemadi  2 , Marina Fernández-Delgado Juárez  1 , María Gómez-Brandón  3 , Heribert Insam  1 , Sabine Marie Podmirseg  1
Affiliations

CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis

Sebastian Hupfauf et al. PLoS One. .

Abstract

In recent years, there has been a veritable boost in next-generation sequencing (NGS) of gene amplicons in biological and medical studies. Huge amounts of data are produced and need to be analyzed adequately. Various online and offline analysis tools are available; however, most of them require extensive expertise in computer science or bioinformatics, and often a Linux-based operating system. Here, we introduce "CoMA-Comparative Microbiome Analysis" as a free and intuitive analysis pipeline for amplicon-sequencing data, compatible with any common operating system. Moreover, the tool offers various useful services including data pre-processing, quality checking, clustering to operational taxonomic units (OTUs), taxonomic assignment, data post-processing, data visualization, and statistical appraisal. The workflow results in highly esthetic and publication-ready graphics, as well as output files in standardized formats (e.g. tab-delimited OTU-table, BIOM, NEWICK tree) that can be used for more sophisticated analyses. The CoMA output was validated by a benchmark test, using three mock communities with different sample characteristics (primer set, amplicon length, diversity). The performance was compared with that of Mothur, QIIME and QIIME2-DADA2, popular packages for NGS data analysis. Furthermore, the functionality of CoMA is demonstrated on a practical example, investigating microbial communities from three different soils (grassland, forest, swamp). All tools performed well in the benchmark test and were able to reveal the majority of all genera in the mock communities. Also for the soil samples, the results of CoMA were congruent to those of the other pipelines, in particular when looking at the key microbial players.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Overview of the CoMA pipeline workflow.
Different colors represent the four sub-sections of the CoMA workflow: Data pre-processing and quality checking (orange), clustering of operational taxonomic units (OTUs) and taxonomic assignment (green), data post-processing (blue) and data visualization and statistical appraisal (yellow). Labelled arrows demonstrate the order of events and name specific file types that are needed as input for each step. Taxonomic assignment is done with Blast, Lambda or RDP using either one of the available databases (e.g. Silva [23]) or any custom database provided by the user. Numbers indicate third party tools that are used for the specific CoMA step: 1 = PANDAseq, 2 = PRINSEQ, 3 = LotuS/sdm, 4 = QIIME, 5 = Mothur. TDOT = Tab-delimited OTU-table. PER = Paired-end reads. SER = Single-end reads. PCoA = Principal coordinates analysis.
Fig 2
Fig 2. Community composition of the mock-13 dataset, revealed with four different analysis platforms.
The set point (SP) depicts the theoretically expected distribution and serves as reference. The dataset comprised 18 bacterial genera, targeted with 16S rRNA amplicon sequencing.
Fig 3
Fig 3. Community composition of the mock-16 dataset, revealed with four different analysis platforms.
The set point (SP) depicts the theoretically expected distribution and serves as reference. The dataset comprised 46 archaeal and bacterial genera, targeted with 16S rRNA amplicon sequencing.
Fig 4
Fig 4. Community composition of the mock-26 dataset, revealed with four different analysis platforms.
The set point (SP) depicts the theoretically expected distribution and serves as reference. The dataset comprised 11 fungal genera, targeted with ITS amplicon sequencing.
Fig 5
Fig 5. Shannon-Wiener diversity (H’) of the three different soils after sequencing data analysis with CoMA, Mothur and QIIME.
Four replicates for each habitat are shown. Letters indicate significant differences across the analysis tools for each habitat. F = forest. GR = grassland. S = swamp.
Fig 6
Fig 6. Principal component analysis based on archaeal and bacterial families of soil samples from three different habitats: Forest, grassland and swamp.
The color code indicates the applied data analysis tool: CoMA, Mothur and QIIME. Q1—Q4 = quadrants of the coordinate system.
Fig 7
Fig 7. Venn plots showing the shared phyla, classes, orders, families and genera found with CoMA, Mothur and QIIME in the soil samples.
Data include all of the three investigated habitats (forest, grassland, swamp).
See this image and copyright information in PMC

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