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Record W3045704485 · doi:10.1101/gr.254219.119

Functional annotation of human long noncoding RNAs via molecular phenotyping

2020· article· en· W3045704485 on OpenAlexafffund
Jordan A. Ramilowski, Chi Wai Yip, Saumya Agrawal, Jen-Chien Chang, Yari Ciani, Ivan V. Kulakovskiy, Mickaël Mendez, Jasmine Li Ching Ooi, John F. Ouyang, Nick Parkinson, Andreas Petri, Leonie Roos, Jessica Severin, Kayoko Yasuzawa, Imad Abugessaisa, Altuna Akalin, Ivan Antonov, Alessandro Bonetti, Hidemasa Bono, Beatrice Borsari, Frank Brombacher, Christopher J. F. Cameron, Carlo Vittorio Cannistraci, Ryan Cardenas, Mélissa Cardon, Howard Y. Chang, Josée Dostie, Luca Ducoli, Alexander V. Favorov, Alexandre Fort, Diego Garrido-Martín, Noa Gil, Juliette Gimenez, Reto Guler, Lusy Handoko, Jayson Harshbarger, Akira Hasegawa, Yuki Hasegawa, Kosuke Hashimoto, Norihito Hayatsu, Peter Heutink, Tetsuro Hirose, Eddie L. Imada, Masayoshi Itoh, Bogumił Kaczkowski, Aditi Kanhere, Emily Kawabata, Hideya Kawaji, Tsugumi Kawashima, S. Thomas Kelly, Miki Kojima, Naoto Kondo, Haruhiko Koseki, Tsukasa Kouno, Anton Kratz, Mariola Kurowska‐Stolarska, Andrew Tae-Jun Kwon, Jeffrey T. Leek, Andreas Lennartsson, Marina Lizio, Fernando Martínez López, Joachim Luginbühl, Shiori Maeda, Vsevolod J. Makeev, Luigi Marchionni, Yulia A. Medvedeva, Aki Minoda, Ferenc Müller, Manuel Muñoz-Aguirre, Mitsuyoshi Murata, Hiromi Nishiyori, Kazuhiro R. Nitta, Shuhei Noguchi, Yukihiko Noro, Ramil Nurtdinov, Yasushi Okazaki, Valerio Orlando, Denis Paquette, Callum Parr, Owen J. L. Rackham, Patrizia Rizzu, Diego F. Sánchez, Albin Sandelin, Sanjana Pillay, Colin A. Semple, Youtaro Shibayama, Divya M. Sivaraman, Takahiro Suzuki, Suzannah C. Szumowski, Michihira Tagami, Martin S. Taylor, Chikashi Terao, Malte Thodberg, Supat Thongjuea, Vidisha Tripathi, Igor Ulitsky, Roberto Verardo, Ilya E. Vorontsov, Chinatsu Yamamoto, Robert S. Young, J. Kenneth Baillie, Alistair R. R. Forrest, Roderic Guigó, Michael M. Hoffman, Chung-Chau Hon, Takeya Kasukawa, Sakari Kauppinen, Juha Kere, Boris Lenhard, Claudio Schneider, Harukazu Suzuki, Ken Yagi, Michiel de Hoon, Jay W. Shin, Piero Carninci

Bibliographic record

VenueGenome Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsPrincess Margaret Cancer CentreInstitute of Infection and ImmunityMcGill UniversityUniversity of Toronto
FundersEuropean Social FundRIKENNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthH. Lundbeck A/SMedical Research CouncilVersus ArthritisMinistry of Education, Culture, Sports, Science and TechnologyRussian Science FoundationGeneralitat de CatalunyaNovo NordiskNational Cancer InstituteMinistero dell’Istruzione, dell’Università e della RicercaVetenskapsrådetLundbeckfondenCancerfondenRussian Academy of SciencesJapan Society for the Promotion of ScienceNational Health and Medical Research CouncilRussian Foundation for Basic ResearchWellcome TrustU.S. Department of Defense
KeywordsBiologyComputational biologyLong non-coding RNAGeneticsHuman genomeAnnotationNon-coding RNAmicroRNARNAGenomeGene

Abstract

fetched live from OpenAlex

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-to-date lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2 .

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.584
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.357
Teacher spread0.281 · 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 teacher head, 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

Citations197
Published2020
Admission routes2
Has abstractyes

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