Elevate Your Microbiome Research with Advanced Statistics
Leverage MaAsLin tools in CosmosID-HUB for accurate, scalable, and reproducible differential abundance analysis.

Why Choose the Advanced Statistics Module?
Interpreting data from complex study designs requires sophisticated algorithms to account for the numerous variables in experimental data. With the Advanced Statistics Module, you can seamlessly implement tools like MaAsLin3 into your workflows to uncover significant associations between microbial communities and host/environmental metadata.
Features
Robust Differential Abundance Analysis
Utilize MaAsLin3 for precise detection of microbiome-metadata relationships, ensuring your research is powered by the most up-to-date computational methods.
User-Friendly Interface
Seamlessly upload raw sequencing data and navigate through click-to-run menus for quick but complex statistical analyses, without the need for extensive bioinformatics expertise.
Scalable
Seamlessly analyze datasets from small environmental studies to large-scale clinical studies.
Compatible with all workflows & applications
16S, Shotgun Metagenomics & Metatranscriptomics
Unparalleled support
Onboarding & Detailed Documentation.
No-code
Leverage your microbiome profiles and metadata in just a few clicks.
Data aggregation and storage
All analyses are automatically saved so users can go back and find their analyses or iterate further.
New and improved data visualizations
What is MaAsLin?
MaAsLin is an advanced tool for conducting differential abundance analysis to identify significant associations between microbial taxa and various metadata variables in longitudinal and cross-sectional studies. MaAsLin applies robust statistical models to detect consistent patterns of microbial abundance relative to environmental or host-related variables, providing insights that are pivotal for understanding microbial community dynamics and their implications on health and disease.
Integration of MaAsLin2 and MaAsLin3 into the CosmosID-HUB enhances user experience by providing an intuitive interface without needing advanced bioinformatics skills. This integration highlights our commitment to offering comprehensive, state-of-the-art tools that streamline microbiome research.







Analyze Microbiome Data in Three Simple Steps
1. Profile your microbiome data within the CosmosID-HUB.

2. Assign metadata and set statistical parameters.

3. Interpret figures and results for taxonomic and functional data.

Case Study: Microbial Associations in Chickens infected with Salmonella enterica
This case study underscores the complexity of microbial dynamics in the context of infection and development. Based on the study published by ASM Journals, the analysis of young chicken cecal samples reveals the differential microbial abundances between infected and uninfected groups over time, adjusted for days post-hatching. While Salmonella enterica and other microbes were higher in the infected group, certain commensal microbes appeared more frequently in the uninfected group, suggesting a protective effect. Understanding these associations, especially over key developmental timelines like days post hatching, provides insights into potential therapeutic or preventive measures targeting microbiome modulation. This analysis demonstrates the advanced capabilities of the CosmosID-HUB platform to decipher intricate microbial interactions and their implications on health.
Key Findings
The data demonstrates a significant increase in the abundance of Salmonella enterica following infection, with marked changes in microbial composition over time. In particular, there was increased abundance and 100% prevalence of Salmonella enterica in the infected group, indicating a complete presence across all sampled entities, underscoring its dominance and possible invasive nature during the infection phase.
- Alongside Salmonella enterica, other bacteria such as Enterobacter ludwigii and Escherichia coli were shown to be higher in the infected animals.
- Ligilactobacillus salivarius and Paenibacillus barengoltzii were more prevalent in the uninfected group, suggesting a potential protective role against infection.
The analysis accounted for the effect of days post hatching, a key developmental period. Microbes like Paenibacillus barengoltzii and Escherichia coli showed an increasing trend over time, which could be indicative of developmental changes in the gut microbiome influenced by external or internal factors.
- The results were adjusted for multiple comparisons using False Discovery Rate (FDR) methods, ensuring that the associations reported are statistically robust. For instance, the presence of Escherichia coli over time showed significant changes with an FDR of 4.2e-03, indicating its dynamic role in microbial community structure.
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Have Questions About MaAsLin3?
MaAsLin3 supports a variety of data types relevant to microbiome research. It can process taxonomic, functional, and antimicrobial resistance data generated by Amplicon, Shotgun Metagenomics, or Metatranscriptomics. This flexibility allows researchers to perform differential abundance analysis across different microbial community datasets and kingdoms. This capability is essential for comprehensive analyses in diverse study designs, from environmental sampling to human microbiome projects.
Yes, you can use MaAsLin3 in conjunction with other tools available on the CosmosID-HUB, such as alpha/beta diversity, abundance charts, and heatmaps. This integration allows you to leverage the full suite of functionalities within the platform from a single comparative analysis, allowing visualization of taxonomic and functional results directly within the CosmosID-HUB environment. This integration simplifies workflows, making it easier for researchers to transition between different types of analyses and utilize the platform’s broad capabilities for a holistic approach to microbiome research.
The cloud-based CosmosID-HUB is designed with simple interfaces within a web browser, requiring little bioinformatics background to run analysis. MaAsLin3 is recognized as an advanced tool within the CosmosID-HUB. To effectively utilize its capabilities, users are expected to have a solid understanding of statistics and statistical tools to achieve the most robust and accurate results. However, to support users of varying expertise levels, the platform provides comprehensive documentation (link https://docs.cosmosid.com/docs/maaslin3-for-differential-abundance) and recommended parameters tailored for most study applications. This guidance helps ensure that all users can leverage the software effectively, regardless of their statistical or bioinformatic background.