Constellab 16S rRNA-seq

The Constellab 16S rRNA-seq is a Streamlit application designed for microbiome data analysis and visualization. It provides an interactive interface for processing, analyzing, and interpreting 16S rRNA sequencing data through various bioinformatics workflows.

This standalone dashboard only allows users to visualise results that have already been run. However, don't hesitate to contact us to arrange a demo of the full app, in which we can also run analyses automatically!

Multi-Step Analysis Pipeline

The dashboard implements a structured analysis workflow with the following steps:

• Metadata table: Create a metadata table from a fastq folder

• Quality Control (QC): Initial data quality assessment
• MultiQC Reporting: Comprehensive quality control reporting
• Feature Inference: ASV/OTU identification and quantification
• Taxonomy Assignment: Taxonomic classification of features
• Rarefaction Analysis: Diversity curve generation
• PCoA (Principal Coordinate Analysis): Beta diversity visualization
• ANCOM (Analysis of Composition of Microbiomes): Differential abundance testing
• Database Annotation: Feature annotation against reference databases
• Functional Prediction: Metabolic pathway inference using tools like PICRUSt2

 In 2019, Pietrucci et al. has published an article about the dysbiosis of gut microbiota in a population of Parkinson's patients. 152 fecal samples were collected from 80 patients and 72 healthy controls. Microbiota composition was studied through 16s ribosomal RNA gene amplicon sequencing analysis in combination with data on dietary/life habits. Metabarcoding sequencing files are freely available in Sequence Read Archive (PRJNA510730). In the dashboard, data are from this study.   

Reference: 

Pietrucci D, Cerroni R, Unida V, Farcomeni A, Pierantozzi M, Mercuri NB, Biocca S, Stefani A, Desideri A. Dysbiosis of gut microbiota in a selected population of Parkinson's patients. Parkinsonism Relat Disord. 2019 Aug;65:124-130. doi: 10.1016/j.parkreldis.2019.06.003. Epub 2019 Jun 3. PMID: 31174953.