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 the 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

Pathogen detection with PE Illumina reads from metagenomic sequencing of sterile-site samples (validated for ocular fluid and cerebrospinal fluid):

nextflow run -r v1.0.0 https://github.com/esteinig/cerebro \
  -profile dgx,large,mamba,cns,cipher \
  -entry pathogen \
  --outputDirectory outputTest/ \
  --databaseDirectory db/ \
  --fastqPaired 'fastq/*_{R1_001,R2_001}.fastq.gz'

This will run the default quality control and taxonomic profiling and metagenome assembly configuration for pathogen identification in high human - low microbial biomass sample types where distinction from contamination is the main challenge for diagnostics (needle-in-a-haystack). It is not suitable for high microbial biomass sample types such as respiratory or environmental samples where a diverse and abundant background microbiome is the mai nchallenge for diagnostics (haystack-full-of-needles).

You will need the Cipher diagnostic databases, which is an amalgamation of archaeal/bacterial, eukaryotic and viral reference genome collections and taxonomies.

Cerebro CLI

Quick start

Clinical reporting

Quick start

Application stack

Quick start

Cerebro API

Quick start

Cerebro FS

Quick start

Databases and taxonomy

Quick start

Production

With the stack running (see below) production mode will upload outputs directly to the team database and

{sample_id}__{tag1}__{tag2}__{tag3}_R1_001.fastq.gz

The most important tags are (forward read examples, matches reverse read file names):

• DNA or RNA for example: DW-63-V01__DNA_R1_001.fastq.gz and a matching DW-63-V01__RNA_R1_001.fastq.gz read file
• NTC and ENV for negative template and environmental controls for example: DW-63-V420__DNA__NTC_R1_001.fastq.gz for the DNA negative template control library
• POS for a positive control mock sample for extraction and sequencing controls for example DW-63-V07__DNA__NTC_R1_001.fastq.gz for the DNA positive control library

Any other tag outside of the above reserved ones can also be added but has not specific functions in the stack.

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