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Talos

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.

A full description of the method and its validation in large clinical and research cohorts is available in our preprint: medRxiv 2025.05.19.25327921.

Note - whether you are a new Talos user, or have an implementation already, we encourage you to run the preparation workflow with each update!

Latest Changes

[11.0.0] - 2026-05-15

Added

  • Added optional Mitochondrial annotation pathway to the NextFlow implementation, and included mito annotation resources in the prep workflow.
  • Full mkdocs build for the companion Docs site.
  • Test fixtures and demonstration data now includes more non-coding variants and Mitochondrial data.
  • Added "Super Logging" functionality. Opt-in high rate logging to explain the rejection reason for each variant.
  • Talos previously only logged results, not rejections. This logs misses, and explanations e.g. threshold failure, family test, insufficient read depth, comp-het with only support categories
  • Using the "confidence_level" configuration setting can allow PanelApp genes with lower evidence levels (1 >= Red, 2 >= Amber) to be used in analysis. The default level remains 3/Green-only.
  • The HTML report shows, for each gene, the panels it was found in, and the confidence level associated evidence level on each panel. Checkboxes can be used to filter to specific confidence levels.

Changed

  • Completed the migration from publishDir directives to exclusively using Workflow Outputs (see https://nextflow.io/docs/latest/tutorials/workflow-outputs.html)
  • Removed some intermediate build layers from Dockerfiles
  • The Ensembl GFF3 file is now edited during the download script, to re-name chrMT -> chrM, which enables Mitochondrial analysis. This requires re-running the file download and preparation workflow.
  • The AlphaMissense category now allows users to set a pathogenic threshold (config.toml -> RunHailFiltering.am_pathogenicity). Each run can now set a manual threshold instead of deferring to the low default value of 0.564.
  • PanelApp parsing can now pull Red, Amber, and Green genes. A config parameter confidence_level can be used to control this behaviour. N.b. MOI may not be as well curated for non-green genes.

NOTE! Since 10.0.0, Talos uses a TSV input file to drive analyses. As of this update, the optional columns (previous results as a history, seqr IDs, secondary IDs, now Mitochondrial VCF) are all truly optional. The workflow doesn't require them to be populated in the input file at all.

Fixed

  • Swapped out the standard BCFtools build for a CPG-fork.
  • This fork contains a single change - coding and non-coding genes are both annotated equally. This behaviour has been upstreamed into BCFtools, but not yet released (see this commit)
  • In our local runs we have seen that the default behaviour of BCFtools (issue here) lead to failure to annotate consequences in non-coding genes where they overlapped with a coding gene, even if the non-coding gene was of greater clinical relevance.
  • By moving to the CPG fork, we have altered this behaviour. This may result in slightly slower annotation times, but should always present both coding and non-coding gene annotations where appropriate.

NOTE! For existing Talos users, obtaining this extra annotation will require the re-run of the annotation workflow. One example gene which is known to be obscured by this default behaviour was RNU2-2, but there may be others.

Full changelog

Where to next

  • Getting Started Install the requirements, download annotation resources, and run your first cohort.

  • Features Logic modules, reanalysis mode, phenotype matching, and what Talos is (and isn't) for.

  • Configuration Full reference for the Talos TOML config and Nextflow parameters.

  • Changelog Release history and version-by-version changes.

When to use Talos

Talos 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 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.

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

@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},
}