The Xenopus ORFeome: A resource that enables functional genomics
Bibliographic record
Abstract
Functional characterisation of proteins and large-scale, systems-level studies are enabled by extensive sets of cloned open reading frames (ORFs) in an easily-accessible format that enables many different applications. Here we report the release of the first stage of the Xenopus ORFeome, which contains 8673 ORFs from the Xenopus Gene Collection (XGC) for Xenopus laevis, cloned into a Gateway® donor vector enabling rapid in-frame transfer of the ORFs to expression vectors. This resource represents an estimated 7871 unique genes, approximately 40% of the non-redundant X. laevis gene complement, and includes 2724 genes where the human ortholog has an association with disease. Transfer into the Gateway system was validated by 5' and 3' end sequencing of the entire collection and protein expression of a set of test clones. In a parallel process, the underlying ORF predictions from the original XGC collection were re-analysed to verify quality and full-length status, identifying those proteins likely to exhibit truncations when translated. These data are integrated into Xenbase, the Xenopus community database, which associates genomic, expression, function and human disease model metadata to each ORF, enabling end-users to search for ORFeome clones with links to commercial distributors of the collection. When coupled with the experimental advantages of Xenopus eggs and embryos, the ORFeome collection represents a valuable resource for functional genomics and disease modelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".