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Record W2904925280 · doi:10.1038/s42003-018-0226-0

Identification of genes required for eye development by high-throughput screening of mouse knockouts

2018· article· en· W2904925280 on OpenAlexafffund
Bret A. Moore, Brian C. Leonard, Lionel Sebbag, Sydney Edwards, Ann Cooper, Denise M. Imai, Ewan Straiton, Luís Santos, Christopher M. Reilly, Stephen M. Griffey, Lynette Bower, David Clary, Jeremy Mason, Michel J. Roux, Hamid Méziane, Yann Hérault, Anna Swan, Ruairidh King, Piia Keskivali-Bond, Lois Kelsey, Igor Vukobradovic, Dawei Qu, Ruolin Guo, Elisa Tran, Lily Morikawa, Milan Ganguly, Napoleon Law, Xueyuan Shang, Patricia Feugas, Yanchun Wang, Yingchun Zhu, Kyle Duffin, Ayexa Ramirez, Patricia Penton, Valerie Laurin, Shannon Clarke, Qing Lan, Gillian Sleep, Amie Creighton, Elsa Jacob, Ozge Danisment, Joanna Joeng, Marina Gertsenstein, Monica Pereira, Sue MacMaster, Sandra Tondat, Tracy Carroll, Jorge Cabezas, Amit Patel, Jane Hunter, Gregory B. Clark, Mohammed Bubshait, D. Craig Miller, Khondoker Sohel, Alexandr Bezginov, Matthew McKay, Kevin Peterson, Leslie O. Goodwin, Rachel Urban, Susan Kales, Rob Hallett, Dong Nguyen-Bresinsky, Timothy Leach, Audrie Seluke, Sara Perkins, Amanda Slater, Rick Bedigian, Leah Rae Donahue, Robert A. Taft, James M. Denegre, Zachery Seavey, Amelia Willett, Lindsay Bates, Leslie Haynes, Julie Creed, Catherine Witmeyer, Willson Roper, James Clark, Pamela Stanley, Samantha Burrill, Jennifer Ryan, Yuichi Obata, Masaru Tamura, Hideki Kaneda, Tamio Furuse, Kimio Kobayashi, Ikuo Miura, Ikuko Yamada, Hiroshi Masuya, Nobuhiko Tanaka, Shinya Ayabe, Atsushi Yoshiki, Valerie E. Vancollie, Francesco Chiani, Chiara Di Pietro, Gianfranco Di Segni, Olga Ermakova, Filomena Ferrara, Paolo Fruscoloni, Alessia Gambadoro, Serena Gastaldi, Elisabetta Golini, Gina La Sala, Silvia Mandillo, Daniela Marazziti, Marzia Massimi, Rafaele Matteoni, Tiziana Orsini, Miriam Pasquini, Marcello Raspa, Aline Rauch, Gianfranco Rossi, Nicoletta Rossi, Sabrina Putti, Ferdinando Scavizzi, Giuseppe D. Tocchini-Valentini, Colin McKerlie, Ann M. Flenniken, Lauryl M. J. Nutter, Zorana Berberovic, Celeste Owen, Susan Newbigging, Hibret A. Adissu, Mohammed Eskandarian, Chih‐Wei Hsu, Sowmya Kalaga, Uchechukwu Udensi, Chinwe Asomugha, Ritu Bohat, Juan Gallegos, John R. Seavitt, Jason D. Heaney, Arthur L. Beaudet, Mary E. Dickinson, Monica J. Justice, Vivek M. Philip, Vivek Kumar, Karen L. Svenson, Robert E. Braun, Sara Wells, Heather Cater, Michelle Stewart, Sharon Clementson-Mobbs, Russell Joynson, Xiang Gao, Tomohiro Suzuki, Shigeharu Wakana, Damian Smedley, Je Kyung Seong, Glauco P. Tocchini‐Valentini, Mark W. Moore, Colin Fletcher, Natasha A. Karp, Ramiro Ramírez‐Solis, Jacqueline K. White, Martin Hrabě de Angelis, Wolfgang Wurst, Sara M. Thomasy, Paul Flicek, Helen Parkinson, Steve D. M. Brown, Terrence F. Meehan, Patsy M. Nishina, Stephen A. Murray, Mark P. Krebs, Ann‐Marie Mallon, K. C. Kent Lloyd, Christopher J. Murphy, Ala Moshiri

Bibliographic record

VenueCommunications Biology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalHospital for Sick ChildrenToronto Centre for Phenogenomics
FundersCommon FundNational Cancer InstituteNational Eye InstituteNational Institute on Drug AbuseMedical Research CouncilGenome CanadaUniversity of California, DavisGovernment of CanadaPHENOMINNational Institutes of HealthOntario GenomicsNational Human Genome Research InstituteWellcome TrustRetina Research FoundationNIH Office of the DirectorFoundation for the National Institutes of Health
KeywordsGene knockoutGeneBiologyPhenotypeComputational biologyCandidate geneGeneticsDiseaseIdentification (biology)Human diseaseGenetic screenBioinformaticsMedicinePathology

Abstract

fetched live from OpenAlex

Despite advances in next generation sequencing technologies, determining the genetic basis of ocular disease remains a major challenge due to the limited access and prohibitive cost of human forward genetics. Thus, less than 4,000 genes currently have available phenotype information for any organ system. Here we report the ophthalmic findings from the International Mouse Phenotyping Consortium, a large-scale functional genetic screen with the goal of generating and phenotyping a null mutant for every mouse gene. Of 4364 genes evaluated, 347 were identified to influence ocular phenotypes, 75% of which are entirely novel in ocular pathology. This discovery greatly increases the current number of genes known to contribute to ophthalmic disease, and it is likely that many of the genes will subsequently prove to be important in human ocular development and disease.

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 imitation

Not 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.321
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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
GenreEmpirical

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

Citations43
Published2018
Admission routes2
Has abstractyes

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