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…rence selection to set it
This function queries the Ensembl API with exponential backoff as needed, returning a list of features which overlap the passed region.
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See #66 for information on computing this information for regulatory targets, which will be included in a future release. |
This was referenced Dec 13, 2025
sallybg
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This is great! The only comment I have is that there are some changes in the router function (src/api/routers/map.py) which we should also recreate in the corresponding command line function (save_mapped_output_json, which is in src/dcd_mapping/annotate.py). I think we just need to add the layers and gene_info properties to the reference_sequences dict when accessing the mapper from the command line, unless you had a reason for not including it there.
Computes a new `gene_info` property for all mapped targets. This property is defined by an `hgnc_symbol` and a `selection_method`. The hgnc symbol is the HGNC symbol of the gene to which this target relates. The selection method is the method by which this symbol was selected and may be: - `tx_selection`: via the selected transcript - `alignment_max_covered_bases`: based on the gene 'feature' (via Ensembl) which covered the most bases of the aligned target - `variants_max_covered_bases`: same as `alignment_max_covered_bases`, but based on variant bases rather than aligned bases - `target_metadata`: based on parsing the target metadata the user supplied - `target_category`: no gene info was selected because the target was not protein coding (see #66) Various helpers were added to `dcd_mapping.annotate` which support this calculation. Gene info selection should not cause job failures, and will simply fail to select gene info on failure.
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The addition of gene info to the command line output looks good! |
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This pull request introduces a new mechanism for inferring and attaching gene symbol information to target annotations, with provenance, in the mapping pipeline. It adds the
compute_target_gene_infofunction, which determines a single gene symbol per target using a prioritized approach, and integrates this logic into the mapping API. Additionally, it restructures thereference_sequencesdata structure to use a newTargetAnnotationclass, and makes several supporting improvements and bug fixes.Gene symbol inference and annotation improvements:
compute_target_gene_infoasync function inannotate.py, which determines a single gene symbol per target using a prioritized strategy (selected transcript, alignment overlap, variant spans, or fallback to metadata), and returns aGeneInfoobject with provenance. Supporting helper functions for overlap-based inference and interval merging were also added.map_scoresetAPI route: for each target, the computed gene info is attached to itsTargetAnnotationin the response. [1] [2]Data structure and schema changes:
reference_sequencesstructure inmap_scoresetto useTargetAnnotationobjects, which now include alayersattribute and a newgene_infofield. Adjusted all code paths to reference the new structure. [1] [2] [3]TargetAnnotationandGeneInfoimports to relevant modules, ensuring the new schema types are properly used throughout. [1] [2]Supporting infrastructure and environment:
ENSEMBL_API_URLto the.env.devsettings file to support Ensembl API queries for gene overlap.request_with_backoffandENSEMBL_API_URLinlookup.pyto enable robust gene feature queries.Anyimport inlookup.pyfor type hinting.Bug fixes and code improvements:
is not Noneinstead of truthiness) in multiple locations (annotate.py). [1] [2] [3]These changes collectively improve the accuracy and transparency of gene symbol assignment in the API, and lay the groundwork for robust, provenance-aware gene annotation in downstream analyses.