Microbiome Profiling

Streamline Microbiome Profiling with Cosmos-Hub

Cosmos-Hub allows researchers to import their raw sequencing data directly into the platform and run a number of available bioinformatics pipelines for microbiome analysis.

Short-Read 16S Amplicon Profiling with Cosmos-Hub

Introduction to Short-Read Amplicon Profiling

Short-read amplicon sequencing has become the gold standard for high-throughput microbiome research, enabling researchers to profile microbial communities with precision and scalability. The method targets specific marker genes (e.g., 16S rRNA , 18S rRNA, ITS region) to characterize taxonomic diversity across environmental, clinical, and industrial samples. Unlike shotgun metagenomics, amplicon sequencing provides cost-effective, focused analysis that can process hundreds of samples simultaneously while maintaining genus-to-species resolution through advanced bioinformatics approaches.

Amplicon analysis has evolved beyond traditional OTU clustering methods to embrace more-exact sequence variant detection. This enables detection of fine-scale genetic variants within microbiomes that were previously masked by clustering approaches, revealing biological variation that directly impacts results.

Microbiome Profiling

Streamline Microbiome Profiling with Cosmos-Hub

Cosmos-Hub allows researchers to import their raw sequencing data directly into the platform and run a number of available bioinformatics pipelines for microbiome analysis.

In just a few easy steps, users can run industry-leading bioinformatics pipelines for a wide range of different data types:

Import data from their computer, from Illumina BaseSpace, directly from NCBI SRA or via Command Line Import (CLI).
Select the type of data they’d like to import: Shotgun, 16S or ITS
Select from 1 of 9 different host genomes to run automated host read depletion
Upload your study metadata
Choose your pipeline and primers

In just a few easy steps, users can run industry-leading bioinformatics pipelines for a wide range of different data types:

Import data from their computer, from Illumina BaseSpace, directly from NCBI SRA or via Command Line Import (CLI).
Select the type of data they’d like to import: Shotgun, 16S or ITS
Select from 1 of 9 different host genomes to run automated host read depletion
Upload your study metadata
Choose your pipeline and primers

The Cosmos-Hub team has integrated the industry-leading DADA2 pipeline for exact sequence variant inference in 16S microbiome data. Cosmos-Hub amplicon profiling advances beyond traditional clustering-based methods through data-driven error correction to distinguish true biological sequences from noise and artifacts.

DADA2 can model sequencing errors directly from each dataset, rather than relying on generic error assumptions. This approach enables the detection of amplicon sequence variants (ASVs) that differ by single nucleotides, providing researchers with exact biological sequences that are universally comparable across studies and platforms. The method delivers superior resolution for ecological analysis, biomarker discovery, and longitudinal studies where precise taxonomic identification is critical.

The Cosmos-Hub implementation streamlines the complete DADA2 workflow through an intuitive, no-code interface that supports data from various sequencing platforms.

The pipeline processes paired-end FASTQ files through quality filtering, error modeling, ASV inference, chimera removal, and taxonomic assignment using the latest SILVA database. Functional prediction is seamlessly integrated through PICRUSt2, enabling researchers to infer metabolic pathways and enzyme functions directly from amplicon data.

Our cloud-based platform addresses common DADA2 implementation challenges through automated quality control, customizable workflows and primers, and platform-agnostic design. Users can upload study metadata, specify primer sequences, while the system handles complex bioinformatics processing and batch normalization across large datasets. This approach eliminates technical barriers while maintaining the analytical rigor that makes DADA2 the preferred method for high-resolution microbiome studies.

Databases and Outputs

The Cosmos-Hub short-read amplicon pipeline leverages the comprehensive SILVA v138.1 database for taxonomic assignment, providing genus-to-species-level identification where possible across bacterial, archaeal, and eukaryotic domains. The platform generates publication-ready outputs including:

ASV Table

Exact sequence variants by sample with read counts, providing universal reference sequences for cross-study comparisons and meta-analyses.

Taxonomic Assignment

ASV-level identification using SILVA database with confidence scoring and phylogenetic placement for ecological interpretation.

Functional Prediction

PICRUSt2-based inference of enzyme families, metabolic pathways, and gene ontology terms from ASV data for hypothesis generation.

Quality Metrics

Comprehensive tracking of reads retained through each processing step, enabling identification of technical issues and data quality assessment.

Comparative Analysis Integration

Direct compatibility with Cosmos-Hub Statistics Toolbox for group comparisons, diversity analysis, and biomarker discovery.

Batch Processing

Automated handling of large sample cohorts with consistent parameter application and technical replicate management.

Pipeline Performance, Benchmarking & References

DADA2 has been extensively validated through rigorous benchmarking studies demonstrating superior accuracy compared to OTU-based methods. The algorithm's data-driven error modeling produces significantly fewer spurious sequences while maintaining high sensitivity for detecting rare taxa. Cosmos-Hub's implementation has been optimized for computational efficiency, enabling linear scaling across large datasets with no local computation requirements. More information on the method and comprehensive performance evaluation can be found in the original Nature Methods publication and extensive community documentation.

Learn More

Simply drag and drop your raw fastq's to upload your samples directly to the platform in a matter of minutes, with support for desktop, API, cloud repositories, and NCBI SRA sources.
Simplified workflows, reducing the number of parameters to be amended, relative to DADA2 in Nextflow (Ampliseq)
Flexible quality thresholds (Q15, Q20, Q25), primer trimming options, and taxonomic assignment parameters tailored to your specific study design and sequencing platform.

Exact sequence variant detection enables genus-to-species-level discrimination and cross-study compatibility, facilitating meta-analyses and large consortium data integration without re-clustering artifacts.
Generate comprehensive ASV tables and functional predictions in hours rather than weeks, with automated quality control and error detection throughout the workflow.
Seamless transition from ASV generation to comparative analysis, diversity calculations, and differential abundance testing within a unified platform environment.

Finally, the Cosmos-Hub Support team provides personalized guidance for primer selection, quality parameter optimization, and technical troubleshooting to ensure successful project completion.

Book a demo

Applications and sample types

Decision-making on when to implement DADA2 short-read amplicon analysis depends on study objectives, sample characteristics, and the need for high-resolution taxonomic profiling with functional inference capabilities.

DADA2 amplicon sequencing excels in applications requiring precise taxonomic resolution and comparative analysis across multiple samples:

Longitudinal microbiome studies requiring consistent, high-resolution tracking of community changes over time with sensitivity for detecting subtle shifts.
Clinical biomarker discovery demanding accurate taxonomic identification with minimal false positives for diagnostic development and therapeutic monitoring applications.

Environmental monitoring needing robust, standardized profiling across diverse sample types with universal ASV comparability for ecosystem assessment and pollution detection.
Population health research requiring scalable, cost-effective analysis of large cohorts with batch normalization capabilities for multi-site studies and demographic comparisons.