Academic literature on the topic 'Dada2 pipeline'

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

Linking community composition and ecosystem function via the cultivation-independent analysis of marker genes, e.g., the 16S rRNA gene, is a staple of microbial ecology and dependent disciplines. The certainty of results, independent of the bioinformatic handling, is imperative for any advances made within the field. In this work, thermophilic anaerobic co-digestion experimental data, together with primary and waste-activated sludge prokaryotic community data, were analyzed with two pipelines that apply different principles when dealing with technical, sequencing, and PCR biases. One pipeline (VSEARCH) employs clustering methods, generating individual operational taxonomic units (OTUs), while the other (DADA2) is based on sequencing error correction algorithms and generates exact amplicon sequence variants (ASVs). The outcomes of both pipelines were compared within the framework of ecological-driven data analysis. Both pipelines provided comparable results that would generally allow for the same interpretations. Yet, the two approaches also delivered community compositions that differed between 6.75% and 10.81% between pipelines. Inconsistencies were also observed linked to biologically driven variability in the samples, which affected the two pipelines differently. These pipeline-dependent differences in taxonomic assignment could lead to different conclusions and interfere with any downstream analysis made for such mis- or not-identified species, e.g., network analysis or predictions of their respective ecosystem service.

An increasing body of evidence highlights the role of fecal microbiota in various human diseases. However, more than two-thirds of fecal bacteria cannot be cultivated by routine laboratory techniques. Thus, physicians and scientists use DNA sequencing and statistical tools to identify associations between bacterial subgroup abundances and disease. However, discrepancies between studies weaken these results. In the present study, we focus on biases that might account for these discrepancies. First, three different DNA extraction methods (G’NOME, QIAGEN, and PROMEGA) were compared with regard to their efficiency, i.e., the quality and quantity of DNA recovered from feces of 10 healthy volunteers. Then, the impact of the DNA extraction method on the bacteria identification and quantification was evaluated using our published cohort of sample subjected to both 16S rRNA sequencing and whole metagenome sequencing (WMS). WMS taxonomical assignation employed the universal marker genes profiler mOTU-v2, which is considered the gold standard. The three standard pipelines for 16S RNA analysis (MALT and MEGAN6, QIIME1, and DADA2) were applied for comparison. Taken together, our results indicate that the G’NOME-based method was optimal in terms of quantity and quality of DNA extracts. 16S rRNA sequence-based identification of abundant bacteria genera showed acceptable congruence with WMS sequencing, with the DADA2 pipeline yielding the highest congruent levels. However, for low abundance genera (<0.5% of the total abundance) two pipelines and/or validation by quantitative polymerase chain reaction (qPCR) or WMS are required. Hence, 16S rRNA sequencing for bacteria identification and quantification in clinical and translational studies should be limited to diagnostic purposes in well-characterized and abundant genera. Additional techniques are warranted for low abundant genera, such as WMS, qPCR, or the use of two bio-informatics pipelines.

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.

Advances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from sequence identity-based clustering (producing Operational Taxonomic Units, OTUs) to denoising methods (producing amplicon sequence variants, ASVs). While denoising methods have several inherent properties that make them desirable compared to clustering-based methods, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e.g. rarefaction) and computing diversity indices. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. Mothur (a clustering method) on 16S rRNA gene amplicon datasets (hypervariable region v4), and compared such effects to the rarefaction of the community table and OTU identity threshold (97% vs. 99%) on the ecological signals detected. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. The choice of the pipeline significantly influenced alpha and beta diversities and changed the ecological signal detected, especially on presence/absence indices such as the richness index and unweighted Unifrac. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. The identification of major classes and genera also revealed significant discrepancies across pipelines. Compared to the pipeline’s effect, OTU threshold and rarefaction had a minimal impact on all measurements.

The taxonomic composition of microbial communities can be assessed using universal marker amplicon sequencing. The most common taxonomic markers are the 16S rDNA for bacterial communities and the internal transcribed spacer (ITS) region for fungal communities, but various other markers are used for barcoding eukaryotes. A crucial step in the bioinformatic analysis of amplicon sequences is the identification of representative sequences. This can be achieved using a clustering approach or by denoising raw sequencing reads. DADA2 is a widely adopted algorithm, released as an R library, that denoises marker-specific amplicons from next-generation sequencing and produces a set of representative sequences referred to as ‘Amplicon Sequence Variants’ (ASV). Here, we present Dadaist2, a modular pipeline, providing a complete suite for the analysis that ranges from raw sequencing reads to the statistics of numerical ecology. Dadaist2 implements a new approach that is specifically optimised for amplicons with variable lengths, such as the fungal ITS. The pipeline focuses on streamlining the data flow from the command line to R, with multiple options for statistical analysis and plotting, both interactive and automatic.

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.

In this study, we examined the total bacterial community associated with ureolytic biomineralization from urine drainage systems. Biomineral samples were obtained from 11 California Department of Transportation public restrooms fitted with waterless, low-flow, or conventional urinals in 2019. Following high throughput 16S rRNA Illumina sequences processed using the DADA2 pipeline, the microbial diversity assessment of 169 biomineral and urine samples resulted in 3,869 reference sequences aggregated as 598 operational taxonomic units (OTUs). Using PERMANOVA testing, we found strong, significant differences between biomineral samples grouped by intrasystem sampling location and urinal type. Biomineral microbial community profiles and alpha diversities differed significantly when controlling for sampling season. Observational statistics revealed that biomineral samples obtained from waterless urinals contained the largest ureC/16S gene copy ratios and were the least diverse urinal type in terms of Shannon indices. Waterless urinal biomineral samples were largely dominated by the Bacilli class (86.1%) compared to low-flow (41.3%) and conventional samples (20.5%), and had the fewest genera that account for less than 2.5% relative abundance per OTU. Our findings are useful for future microbial ecology studies of urine source-separation technologies, as we have established a comparative basis using a large sample size and study area.

16S rRNA gene sequencing is a cost-effective method for profiling the bacterial component of a microbiome. Nevertheless, processing and analysis of the resulting sequencing data is often constrained by the availability of dedicated bioinformaticians - creating a bottleneck for biological interpretation. Multiple visualisation and analysis tools now exist for downstream analysis of 16S rRNA data. These tools are designed with biological scientists in mind and therefore consist of a graphical user interface that interacts with taxonomic counts tables to perform tasks such as alpha- and beta-diversity analysis and differential abundance. However, generating the input to these applications still relies on bioinformatics experience, creating a disconnect between data processing and data analysis. We aimed to bridge the gap between data processing and data analysis. To do this we have created two tools - OCMS_16S and OCMSlooksy - that perform data processing and data visualisation/analysis, respectively. OCMS_16S is a cgat-core based pipeline that wraps DADA2 functionality in order to facilitate processing of raw sequence reads into tables of amplicon sequence variant (ASV) counts using a simple command line interface. OCMSlooksy is an RShiny application that takes an OCMS_16S-generated SQLite database as input to facilitate data exploration and analysis. Combining these tools provides a simple, user-friendly workflow to facilitate 16S rRNA gene amplicon sequencing data analysis from raw reads to results.

Microbes are fundamental to Earth’s ecosystems, thus understanding ecosystem connectivity through microbial dispersal is key to predicting future ecosystem changes in a warming world. However, aerial microbial dispersal remains poorly understood. Few studies have been performed on bioaerosols (microorganisms and biological fragments suspended in the atmosphere), despite them harboring pathogens and allergens. Most environmental microbes grow poorly in culture, therefore molecular approaches are required to characterize aerial diversity. Bioinformatic tools are needed for processing the next generation sequencing (NGS) data generated from these molecular approaches; however, there are numerous options and choices in the process. These choices can markedly affect key aspects of the data output including relative abundances, diversity, and taxonomy. Bioaerosol samples have relatively little DNA, and often contain novel and proportionally high levels of contaminant organisms, that are difficult to identify. Therefore, bioinformatics choices are of crucial importance. A bioaerosol dataset for bacteria and fungi based on the 16S rRNA gene (16S) and internal transcribed spacer (ITS) DNA sequencing from parks in the metropolitan area of Auckland, Aotearoa New Zealand was used to develop a process for determining the bioinformatics pipeline that would maximize the data amount and quality generated. Two popular tools (Dada2 and USEARCH) were compared for amplicon sequence variant (ASV) inference and generation of an ASV table. A scorecard was created and used to assess multiple outputs and make systematic choices about the most suitable option. The read number and ASVs were assessed, alpha diversity was calculated (Hill numbers), beta diversity (Bray–Curtis distances), differential abundance by site and consistency of ASVs were considered. USEARCH was selected, due to higher consistency in ASVs identified and greater read counts. Taxonomic assignment is highly dependent on the taxonomic database used. Two popular taxonomy databases were compared in terms of number and confidence of assignments, and a combined approach developed that uses information in both databases to maximize the number and confidence of taxonomic assignments. This approach increased the assignment rate by 12–15%, depending on amplicon and the overall assignment was 77% for bacteria and 47% for fungi. Assessment of decontamination using “decontam” and “microDecon” was performed, based on review of ASVs identified as contaminants by each and consideration of the probability of them being legitimate members of the bioaerosol community. For this example, “microDecon’s” subtraction approach for removing background contamination was selected. This study demonstrates a systematic approach to determining the optimal bioinformatics pipeline using a multi-criteria scorecard for microbial bioaerosol data. Example code in the R environment for this data processing pipeline is provided.