pirl-unc / hitlist / 25336096925
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coverage: 71.478% (-0.01%) from 71.489%
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v1.30.34: is_chimeric flag for engineered cross-species systems (#224)

* v1.30.34: is_chimeric flag for engineered cross-species systems (#46 down-payment)

Adds a per-row ``is_chimeric`` boolean to the observations export and
training table that flags rows where the proteome species and the MHC
species come from different vertebrate-host genera — HLA-Tg rats, NetH2pan
binding-prediction training data (human peptides on mouse MHC), AAV-
transduced allogeneic HLA expression, primate / canine xenogeneic
systems, etc.

The full data-model overhaul described in #46 (host_organism,
effector_organism, mhc_donor_individual, etc.) is much larger; this is a
small derived flag that surfaces the ~70K chimeric rows already in the
corpus to downstream consumers without changing the schema contract.

Implementation:

- ``hitlist.curation.is_chimeric_system(source_organism, mhc_species)``:
  cached per-string classifier. Uses ``normalize_species`` to canonicalize
  inputs and a closed whitelist of MHC-bearing vertebrate host genera
  (homo, mus, rattus, canis, felis, sus, bos, equus, gallus, anas, macaca,
  pan, xenopus, sarcophilus, trichosurus, danio, ovis, capra, oryctolagus,
  mesocricetus, cricetulus, cavia). Returns True only when both sides
  resolve to host genera AND the genera differ — pathogen-on-host (viral,
  bacterial, parasite source) and unrecognized species are not chimeric.
- ``hitlist.export._compute_is_chimeric``: vectorized helper that maps the
  classifier across the unique (source, mhc) pairs and broadcasts back.
- Wired into ``generate_observations_table`` after the
  ``has_peptide_level_allele`` block, and into ``_apply_training_defaults``
  so the training-export surface inherits the column.

Coverage on the v1.30.33 production index (Class-I observations):
- 66,869 rows flagged chimeric (~2.2% of the table)
- top buckets: 54,961 HLA-Tg rat rows (PMID 28188227), 10,124 NetH2pan
  rows (PMID 29615400), 879 primate-MHC rows (PMID 26811146), ... (continued)

3902 of 5459 relevant lines covered (71.48%)

0.71 hits per line

Coverage Regressions

Lines Coverage ∆ File
121
80.91
 -0.75% export.py
36
88.81
 0.53% curation.py
Jobs
ID Job ID Ran Files Coverage
1 25336096925.1 28
71.48
 GitHub Action Run
Source Files on build 25336096925