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A resource of targeted mutant mouse lines for 5,061 genes
Marie‐Christine Birling, Atsushi Yoshiki, David J. Adams, Shinya Ayabe, Arthur L. Beaudet, Joanna Bottomley, Allan Bradley, Steve D. M. Brown, Antje Bürger, Wendy Bushell, Francesco Chiani, Hsian‐Jean Chin, Skevoulla Christou, Gemma Codner, Francesco J. DeMayo, Mary E. Dickinson, Brendan Doe, Leah Rae Donahue, Martin Fray, Alessia Gambadoro, Xiang Gao, Marina Gertsenstein, Alba Gomez-Segura, Leslie O. Goodwin, Jason D. Heaney, Yann Hérault, Martin Hrabě de Angelis, Si‐Tse Jiang, Monica J. Justice, Petr Kašpárek, Ruairidh King, Ralf Kühn, Ho Lee, Young Jae Lee, Zhiwei Liu, K. C. Kent Lloyd, Isabel Lorenzo, Ann‐Marie Mallon, Colin McKerlie, Terrence F. Meehan, Violeta Muñoz‐Fuentes, Stuart Newman, Lauryl M. J. Nutter, Goo Taeg Oh, Guillaume Pavlovic, Ramiro Ramírez‐Solis, Barry P. Rosen, Edward J. Ryder, Luís Santos, Joel Schick, John R. Seavitt, Radislav Sedláček, Claudia Seisenberger, Je Kyung Seong, William C. Skarnes, Tania Sorg, Karen P. Steel, Masaru Tamura, Glauco P. Tocchini‐Valentini, Chi‐Kuang Leo Wang, Hannah Wardle‐Jones, Marie Wattenhofer‐Donzé, Sara Wells, Michael V. Wiles, Brandon Willis, Joshua A. Wood, Wolfgang Wurst, Ying Xu, Juan Gallegos, Jennie R. Green, Ritu Bohat, Katie Zimmel, Monica Pereira, S. MacMaster, Sandra Tondat, Linda Wei, Tracy Carroll, Jorge Cabezas, Qing Fan-Lan, Elsa Jacob, Amie Creighton, Patricia Castellanos-Penton, Ozge Danisment, Shannon Clarke, Joanna Joeng, Deborah F. Kelly, Christine To, Rebekah van Bruggen, Valerie Gailus-Durner, Helmut Fuchs, Susan Marschall, Stefanie Dunst, Markus Romberger, Sabine Fessele, Philipp Gormanns, Roland H. Friedel, Cornelia Kaloff, Andreas Hörlein, Adriane Tasdemir, Anne Könitzer, Sarah Weber, Joachim Beig, Matthew Mckay, Richard Bedigian, Stephanie Dion, Peter M. Kutny, Jennifer A. Kelmenson, Emily Perry, Dong Nguyen-Bresinsky, Audrie Seluke, Timothy Leach, Sara Perkins, Amanda Slater, Rachel Urban, Susan Kales, Michael DaCosta, Michael J. McFarland, Rick Palazola, Kevin A. Peterson, Karen L. Svenson, Robert A. Taft, Mark Rhue, Dave Clary, Renee Araiza, Kristin Grimsrud, Lynette Bower, Nicole L. Anchell, Kathrin Jäger, Diana L. Young, Phuong Dao, Wendy Gardiner, Janet Kenyon, Michelle Stewart, Denise Lynch, Jorik Loeffler, Adam Caulder, Rosie Hillier, Mohamed Mohideen Quwailid, Rumana Zaman, Yuichi Obata, Mizuho Iwama, Hatsumi Nakata, Tomomi Hashimoto, Masayo Kadota, Hiroshi Masuya, Nobuhiko Tanaka, Ikuo Miura, Ikuko Yamada, Tamio Furuse, Mohammed Selloum, Sylvie Jacquot, Abdel Ayadi, Dalila Ali-Hadji, Philippe Charles, Elise Le Marchand, Amal El Amri, Christelle Kujath, Jean-Victor Fougerolle, Peggy Mellul, Sandrine Legeay, Laurent Vasseur, Romain Lorentz, Laurence Schaeffer, Dominique Dreyer, Valérie Erbs, Benjamin Eisenmann, Giovanni Battista Rossi, Laurence Luppi, Annelyse Mertz, Amélie Jeanblanc, Evelyn Grau, Caroline Sinclair, Ellen Brown, Alla Madich, Mike Woods, Laila Pearson, Danielle Mayhew, Nicola Griggs, Richard Houghton, James Bussell, Catherine Ingle, Sara Valentini, Diane Gleeson, Debarati Sethi, Tanya Bayzetinova, Jonathan Burvill, Bishoy Habib, Lauren Weavers, Ryea Maswood, Evelina Miklejewska, Ross Cook, Radka Platte, Stacey Price, Sapna Vyas, Adam Collinson, Matt Hardy, Priya Dalvi, Vivek Iyer, Tony West, Mark Thomas, Alejandro O. Mujica, Elodie Sins, Daniel M. Barrett, Michael S. Dobbie, Anne Grobler, Glaudina Loots, Rose Hayeshi, Liezl-Marie Scholtz, Cor Bester, Wihan Pheiffer, Kobus Venter, Fátima Bosch, Lydia Teboul, Stephen A. Murray
Bibliographic record
VenueNature Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsHospital for Sick ChildrenToronto Centre for Phenogenomics
FundersNational Human Genome Research InstituteMedical Research CouncilNational Cancer InstituteNational Institutes of HealthAgence Nationale de la RechercheWellcome Trust
KeywordsMutantBiologyGeneGeneticsAlleleNull alleleReporter geneComputational biologyGene targetingGene expression
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.053
Threshold uncertainty score0.178
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.039 |
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.
Opus teacher head0.007
GPT teacher head0.274
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation status
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.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreDataset
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".
Quick stats
Citations114
Published2021
Admission routes1
Has abstractno