{"id":"W3186869997","doi":"10.1101/2021.07.17.452734","title":"Genome-Wide Detection of Imprinted Differentially Methylated Regions Using Nanopore Sequencing","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Syndromes and Imprinting","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"University of British Columbia; Canada Research Chairs","keywords":"Genomic imprinting; Imprinting (psychology); Differentially methylated regions; Biology; DNA methylation; Nanopore sequencing; Genetics; Methylated DNA immunoprecipitation; CpG site; Genome; Computational biology; Epigenetics; Germline; Methylation; Gene; Gene expression","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000408162,0.0005745054,0.0006603539,0.0002281094,0.0001639005,0.0001270066,0.0005019742,0.000835865,0.00002728952],"category_scores_gemma":[0.000345955,0.0006615405,0.0003750648,0.0003411103,0.0001258988,0.000009325184,0.001081221,0.0005129256,0.000002916455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000185602,"about_ca_system_score_gemma":0.0008976084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001611503,"about_ca_topic_score_gemma":0.00001436681,"domain_scores_codex":[0.9969477,0.0002108341,0.000805524,0.001155925,0.0003079986,0.0005720181],"domain_scores_gemma":[0.9966869,0.00002062651,0.0007570498,0.001476116,0.0008481948,0.0002110649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000995556,0.00005366704,0.003505465,0.0002917923,0.0003779147,0.00001241505,0.00001139135,0.0004062199,0.9953229,0.000004082583,0.000001072147,0.000003164945],"study_design_scores_gemma":[0.0003401552,0.0000777712,0.0894644,0.0002450665,0.0001930681,2.606151e-7,0.00001985652,0.000622596,0.9082811,9.535806e-7,0.0001600065,0.0005947954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9489431,0.00166264,0.04801681,0.00001667876,0.0007938162,0.0004494507,0.0000438058,0.00006916741,0.000004536618],"genre_scores_gemma":[0.9907192,0.0003883502,0.008408867,0.00004072787,0.0002434077,0.00003864442,0.000003843269,0.0001507172,0.000006232896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0870418,"threshold_uncertainty_score":0.9995836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622853756588703,"score_gpt":0.2219647045147862,"score_spread":0.2057361669488992,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}