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 been the go-to method for high-throughput microbiome research, enabling researchers to profile microbial communities with 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. Amplicon sequencing offers specific advantages such as cost-effectiveness, host DNA avoidance and accurate overview analysis of hundreds or thousands of samples simultaneously while maintaining genus-to-species resolution.

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

Cosmos-Hub hosts the industry-leading and very well-cited DADA2 pipeline for short-read 16S data, a tool which advances beyond traditional clustering-based methods and 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 SILVA 138 database. Functional prediction is seamlessly integrated through PICRUSt2, enabling researchers to infer metabolic pathways and enzyme functions directly from amplicon data.

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.

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Direct Fastq Upload via desktop, Basespace, API, or NCBI SRA sources.
Simplified and optimized workflow, reducing the number of parameters to be amended, relative to DADA2 in Nextflow (Ampliseq).
Flexible quality thresholds (Q15, Q20, Q25) and primer selection to suit your study design and sequencing platform.

Seamless combination of DADA2 & PICRUST2 to reveal taxonomy and function.
Generate comprehensive ASV tables and functional predictions in a matter of hours, saving time on your study.
Results can be imported directly into to the Cosmos-Hub Statistics Toolbox and combined with study metadata for downstream statistics and visualizations.

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

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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.