Talos

Overview

Talos is a scalable, open-source variant prioritisation tool designed to support automated reanalysis of genomic data in rare disease. It identifies candidate causative variants in known disease genes by integrating static annotations (e.g. population frequency, predicted consequence) with dynamic knowledge sources such as ClinVar and PanelApp Australia. Talos applies a set of configurable, rule-based logic modules aligned with ACMG/AMP criteria and prioritises variants consistent with expected mode of inheritance and, optionally, patient phenotype.

While Talos can be used for one-off reanalysis of individual families or cohorts, its core design is optimised for routine, cohort-scale reanalysis. By comparing current annotations with prior results, Talos highlights variants that have become reportable due to newly available evidence—such as new gene–disease or variant–disease relationships—since the last analysis cycle. This enables timely identification of new diagnoses driven by emerging knowledge, while maintaining a low manual review burden.

Talos is specifically intended to identify variants in established disease genes that are likely to explain the participant’s condition. It is not designed to detect novel candidate genes or to interpret variants of uncertain significance outside the context of existing clinical knowledge. This focus improves specificity and supports use in diagnostic and research reanalysis workflows.

A full description of the method and its validation in large clinical and research cohorts is available in our preprint:

https://www.medrxiv.org/content/10.1101/2025.05.19.25327921

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When to Use Talos

Talos is designed to support automated reanalysis of rare disease cohorts, enabling identification of candidate causative variants in known disease genes based on the latest available evidence. It is best suited for scenarios where:

• You are performing routine reanalysis of undiagnosed individuals (e.g. monthly or quarterly)

• You want to detect variants that have become reportable due to updates in gene–disease or variant–disease knowledge

• You aim to minimise the number of variants requiring manual review optimising for specificity over sensitivity

• You are working with exome or genome sequencing data from previously analysed research or clinical cohorts

• You need a scalable, reproducible pipeline for family-based or cohort-scale analysis

Talos is not currently designed for:

• Identifying novel candidate disease genes or gene discovery

• Analysing mitochondrial variants, short tandem repeats (STRs), mosaic variants, or variants outside standard clinical reporting regions

Support for some of these variant types may be added in future releases.

Talos complements existing variant curation workflows by focusing on high-specificity identification of variants that are likely to explain a participant’s condition, based on established gene–disease associations and up-to-date variant-level evidence.

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🚀 Quick Start

Talos is implemented using Nextflow, with all dependencies containerised via Docker. The example workflows can be run locally or on a cluster.

1. Install Requirements

• Nextflow

• Docker

To build the Docker image:

docker build -t talos:8.1.0 .

2. Download Annotation Resources

Talos requires several large external resources (e.g. reference genome, gnomAD, AlphaMissense, Phenotype data). These are expected in a large_files directory. See large_files/README.md for detail on where to obtain them, and a script which will handle the initial download of all required resources.

3. Run Annotation Workflow

This step pre-processes and annotates variants. This workflow only needs to be run once per dataset. This can either begin with single-sample VCFs, or a pre-merged multi-sample VCF. If you have a pre-merged VCF, pass into the workflow with --merged_vcf <path> (AC/AF/AN values for each variant will be added to the VCF based on the subset of samples present. If you would like to provide this allele frequency data from an alternate source please raise an issue, and we can look into this):

nextflow -c nextflow/annotation.config \
  run nextflow/annotation.nf \
  [--large_files <path>] \
  [--processed_annotations <path>] \
  [--merged_vcf <path>]

╰─➤  nextflow -c nextflow/annotation.config run nextflow/annotation.nf -with-report local_annotation.html

 N E X T F L O W   ~  version 25.04.6

Launching `nextflow/annotation.nf` [clever_gutenberg] DSL2 - revision: 0db0088239

executor >  local (6)
[d4/943f57] process > MergeVcfsWithBcftools (1)             [100%] 1 of 1 ✔
[6c/2357d2] process > MakeSitesOnlyVcfWithBcftools (1)      [100%] 1 of 1 ✔
[bf/82d4d4] process > AnnotateGnomadAfWithEchtvar (1)       [100%] 1 of 1 ✔
[54/17d1b2] process > AnnotateCsqWithBcftools (1)           [100%] 1 of 1 ✔
[c7/dacf20] process > ReformatAnnotatedVcfIntoHailTable (1) [100%] 1 of 1 ✔
[65/ace33a] process > TransferAnnotationsToMatrixTable (1)  [100%] 1 of 1 ✔
Completed at: 20-Aug-2025 11:24:04
Duration    : 10m 50s
CPU hours   : 0.2
Succeeded   : 6

4. Run Variant Prioritisation

After annotation is complete, run the Talos prioritisation workflow. This workflow should be re-run regularly for an updated analysis:

nextflow -c nextflow/talos.config \
  run nextflow/talos.nf \
  --matrix_table nextflow/cohort_outputs/cohort.mt [--ext_id_map path/to/ext_id_map.tsv]

╰─➤  nextflow -c nextflow/talos.config run nextflow/talos.nf --matrix_table nextflow/cohort_outputs/cohort.mt -with-report talos_run.html

 N E X T F L O W   ~  version 25.04.6

Launching `nextflow/talos.nf` [focused_murdock] DSL2 - revision: 40d4509d71

executor >  local (6)
[bd/99ef0c] process > StartupChecks (1)         [100%] 1 of 1 ✔
[f2/68d5b8] process > UnifiedPanelAppParser (1) [100%] 1 of 1 ✔
[b5/447858] process > RunHailFiltering (1)      [100%] 1 of 1 ✔
[0b/5aa7fd] process > ValidateMOI (1)           [100%] 1 of 1 ✔
[1f/84ddaa] process > HPOFlagging (1)           [100%] 1 of 1 ✔
[9a/f2a945] process > CreateTalosHTML (1)       [100%] 1 of 1 ✔
Completed at: 20-Aug-2025 11:33:23
Duration    : 7m 16s
CPU hours   : 0.1
Succeeded   : 6

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📥 Inputs

Talos requires the following inputs:

Input type	Description
Variant data	Either: a set of individual sample VCFs, or a pre-merged multi-sample VCF. Using the example workflow, providing individual sample VCFs is only intended for relatively small numbers of samples per analysis. Using a pre-merged, normalised multi-sample VCF will be more efficient and scale to much larger sample numbers.
Pedigree file	A tsv file describing family structure, and optionally phenotypic terms per-participant. See details here.
Configuration file	A .toml config file specifying all workflow settings. See example_config.toml for an example, and the Configuration README for a full breakdown of all config parameters
Annotation files	Various, incuding ClinvArbitration data, gnomAD frequencies, Ensembl GTF, and MANE gene data. These are downloaded by running the large_files setup script.

🔬 Input Validation

The first step of the Talos workflow is a module called StartupChecks, which runs a number of input validations:

1. Checks a config file is present, and checks all required entries are present and have the correct type
2. Opens the Matrix Table and checks the schema and data types
3. Parses the Pedigree file and checks that it's well formatted and affected participants are present
4. Checks the ClinVar data, ensuring it is recent and has sufficient entries

This module will either run and complete, or run and fail, printing a collection of all encountered errors. If it fails, you will need to fix the errors before restarting the workflow.

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⚙️ Configuration

Talos as an application is configured through a single TOML file. This contains all thresholds and parameters for the steps of the Talos workflow. See example_config.toml as a baseline example, and Configuration.md for extended details on the role of each parameter, and its default value.

Talos as a Nextflow workflow is configured through configuration files, one each for the annotation and talos stages of the workflow. These configurations define the cohort name, paths to input and annotation resources, and runtime parameters. NextflowConfiguration.md contains a full description of the default values and role in the analysis.

📄 Outputs

Talos produces structured outputs to support both manual review and downstream integration.

Primary Output:

• *.json file listing all candidate variants for each proband

• Includes variant-level and gene-level evidence, inheritance checks, and phenotype match tags

Optional Outputs:

• HTML reports summarising results for analysts or clinicians

• Simplified TSV for Seqr ingestion via MinimiseOutputForSeqr

Reanalysis Metadata:

• first_seen: when the variant was first returned

• evidence_last_updated: when its evidence last changed

Only variants passing configured thresholds and logic modules are returned.

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🔁 Reanalysis Mode

Talos is designed to support automated, iterative reanalysis of undiagnosed cohorts. To do this it reads the results of previous analyses, and integrates them into the latest report. This is currently done by reading in prior analysis results, and incorporating the previous observations with each run. To use this behaviour, use the config setting params.previous_results. See History for more information.

How it works:

1. Run full annotation + prioritisation once

2. In future cycles, update ClinVar / PanelApp only

3. Rerun prioritisation to return newly supported variants

By integrating the results of previous analyses with each new run, each variant in the output includes:

• first_seen: original detection date

• evidence_last_updated: last evidence update (ClinVar, PanelApp)

Talos maintains low review burden by allowing users to filter to only variants with newly actionable evidence in each analysis.

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🧬 Phenotype Matching

Talos supports phenotype-driven filtering using HPO terms. See Pedigree.md for details on how to provide phenotype data in the pedigree file.

Matching Strategies:

• Patient-to-Gene: semantic similarity between HPO terms and gene annotations

• Patient-to-Panel: PanelApp panels assigned if patient terms match panel HPO tags

• Cohort-to-Panel: manually assign panels to all individuals in config

When provided, phenotype terms are used to:

• Build a more accurate gene panel for each analysis, working with PanelApp to match disease-focused panels to HPO terms
• Prioritise variants in the HTML by highlighting variants in genes which are on disease-specific panels, or where the participant and Gene HPO termsets share phenotypic similarities

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🧠 Variant Logic Modules

Talos prioritises variants using rule-based logic modules, each aligned with specific ACMG/AMP evidence criteria.

Module Types

• Primary: sufficient to trigger reporting on their own (e.g. ClinVar_PLP)

• Supporting: used only as second hits in recessive genes (e.g. AlphaMissense)

Standard Modules

Module	Description
ClinVar P/LP	Pathogenic or Likely Pathogenic by ClinvArbitration
ClinVar Recent Gene	P/LP in a PanelApp “new” gene (became Green within the recency window configured by GeneratePanelData.within_x_months, default 24)
High Impact	Predicted high-impact protein consequences
De Novo	Confirmed de novo in affected individual
PM5	Missense in codon with known pathogenic variant
LofSV	Predicted loss-of-function structural variant
ClinVar 0-star	P/LP with 0 gold stars in ClinVar [Supporting category]
AlphaMissense	AlphaMissense-predicted pathogenic missense variant [Supporting category]

Each module can be configured through the .toml config file (see Configuration.md)

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📓 Citation

If you use Talos in your research or clinical workflow, please cite:

Welland MJ, Ahlquist KD, De Fazio P, et al. Scalable automated reanalysis of genomic data in research and clinical rare disease cohorts. medRxiv 2025.05.19.25327921; https://doi.org/10.1101/2025.05.19.25327921

BibTeX:

@article{welland2025talos,
  title     = {Scalable automated reanalysis of genomic data in research and clinical rare disease cohorts},
  author    = {Welland, Matthew J and Ahlquist, KD and De Fazio, Paul and Austin-Tse, Christina and Pais, Lynn and Wedd, Laura and Bryen, Samantha and Rius, Rocio and Franklin, Michael and Hall, Giles and et al.},
  journal   = {medRxiv},
  year      = {2025},
  doi       = {10.1101/2025.05.19.25327921},
  url       = {https://www.medrxiv.org/content/10.1101/2025.05.19.25327921},
}