Bias-invariant RNA-sequencing metadata annotation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34553213", "kind": "science-record", "revision": 7, "content_hash": "sha256:50de0180c12bb124558bc476e32c4ce1de49fa0b4b1c5eac6e90ea382b66ffdb", "created_at": "2026-11-01T15:41:53Z", "updated_at": "2026-11-01T22:04:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bias-invariant RNA-sequencing metadata annotation.", "doi": "10.1093/gigascience/giab064", "pmid": "34553213", "authors": ["Hannes Wartmann", "Sven Heins", "Karin Kloiber", "Stefan Bonn"], "journal": "GigaScience", "year": 2021, "abstract": "BACKGROUND: Recent technological advances have resulted in an unprecedented increase in publicly available biomedical data, yet the reuse of the data is often precluded by experimental bias and a lack of annotation depth and consistency. Missing annotations makes it impossible for researchers to find datasets specific to their needs.\nFINDINGS: Here, we investigate RNA-sequencing metadata prediction based on gene expression values. We present a deep-learning-based domain adaptation algorithm for the automatic annotation of RNA-sequencing metadata. We show, in multiple experiments, that our model is better at integrating heterogeneous training data compared with existing linear regression-based approaches, resulting in improved tissue type classification. By using a model architecture similar to Siamese networks, the algorithm can learn biases from datasets with few samples.\nCONCLUSION: Using our novel domain adaptation approach, we achieved metadata annotation accuracies up to 15.7% better than a previously published method. Using the best model, we provide a list of >10,000 novel tissue and sex label annotations for 8,495 unique SRA samples. 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