Cerebro
Metagenomic diagnostics pipeline and collaborative reporting stack for pathogen detection, species identification, host genome analysis, quality assurance and deployment in clinical and public health production environments.
🩸 Metagenomic diagnostic core functions
Main
- Multi-classifier taxonomic profiling, metagenome assembly and alignment in Nextflow pipelines
- Optimized pangenome host depletion and background depletion with [`Scrubby`]() and `Metabuli`/`Strobealign`
- Viral infections, pan-viral enrichment protocols and syndrome-specific subtyping panels using [`Vircov`]()
- Differential host tumor DNA diagnostics using segmental CNV detection and methylation classifiers ([`Sturgeon`]())
Support
- Species identification pipelines with [`GTDB`]() for prokaryotic ONT/Illumina reference level genomes
- MAG recovery from enriched culture and sample co-assembly, unclassified viral bin prediction ([`geNomad`, `RdRP`]())
- Custom database and index construction, grafted taxonomies, genome cleaning and syndromic diversity injection with [`Cipher`]()
📰 Collaborative clinical reporting (Bug Board)
- [Collaborative and auditable pathogen determination]() from metagenome sequencing results
- Multi-tenant Svelte application and API with secure local or web-server deployment configs
- Scalable application stack deployment with different data security and collaboration models
- Stack configuration and deployment integrated into the primary command-line interface ([Cerebro CLI]())
- Clinical reporting with [`Typst`]() formatted templates linked into the database of evidence from multi-classifier/databases
- Secure [`wasm` enabled report generation]() in-browser for sensitive reports, interactive data visualizations
- Auditable team member comments and results discussion for expert panel reviews of data ([online "Bug Board"]())
🏥 Clinical and public health production environments
- Simulations using in silico syndromic reference panels for ONT/Illumina signal-level and read-level data with [`Cipher`]()
- Evaluation of simulation and patient datasets for continous integration of quality assurance with [`Cipher`]() and [`Cerebro`]()
- Background/sample site/kitome contamination issues in general clinical or public health environments via the Cerebro API
- Distributed sequence and analysis storage, file system and data retention policies, cloud storage etc. through [`SeaweedFS`]() integration
- [Standard operating procedures]() for continous operation of `Cerebro` as a service for clinical diagnostic reporting
- Experimental protocols for reference labs for optimisation of the [UMI-adapter DNA/RNA protocol]() for low abundance clinical sample types
Getting started
Let's step through some common tasks and core functions of Cerebro and its data application and reporting stack. This section provides some examples of how to get started quickly with Cerebro. For more details and how to deploy and operate the full application in production please see the [documentation]().
Minimum requirements:
- Linux OS
- Nextflow v2024.04
- Conda/Mamba/Docker
Computational resource requirements are variable and range from a standard laptop for the application stack to full nation-wide server infrastructure for pipelines and web-application (if you were so inclined). This is because the application stack for data and reporting can be deployed with various [infrastructure, data security and collaboration models]() in mind and depends on the number of laboratories, collaborators, sequencing throughput, data storage and many other considerations.
[!NOTE]
You do not need the Docker stack for core metagenome diagnostic pipelines and report generation - you can run the [Nextflow pipelines]() separately and use the Cerebro CLI for data manipulation, processing of pipeline outputs and clinical report generation.
Nextflow pipeline
Quick start
Cerebro CLI
Quick start
Clinical reporting
Quick start
Application stack
Quick start
Cerebro API and FS
Quick start
Databases and taxonomy
Quick start
Status
Under active development for production release. Not recommended for deployment at this stage.
This is a preliminary public release of code for the viral enrichment branch of the pipeline used in:
Michael A Moso, George Taiaroa, Eike Steinig, Madiyar Zhanduisenov, Grace Butel-Simoes, Ivana Savic, Mona L Taouk, Socheata Chea, Jean Moselen, Jacinta O’Keefe, Jacqueline Prestedge, Georgina L Pollock, Mohammad Khan, Katherine Soloczynskyj, Janath Fernando, Genevieve E Martin, Leon Caly, Ian G Barr, Thomas Tran, Julian Druce, Chuan K Lim, Deborah A Williamson - Non-SARS-CoV-2 respiratory viral detection and whole genome sequencing from COVID-19 rapid antigen test devices: a laboratory evaluation study - Lancet Microbe (2024) -10.1016/S2666-5247(23)00375-0
Pipeline Testing
# Check for errors during development - this will print the startup and completion
# messages to the console and exit the pipeline execution gracefully if not errors
# were found:
nextflow run cerebro/ -profile test_dev
# Check for input checking with minimal database configurations for quality control
# with the human reference database index and
nextflow run cerebro/ -profile db,db_ont,test_io
nextflow run cerebro/ -profile db,db_sr,test_io