{"id":"W4360977027","doi":"10.1101/2023.03.24.534183","title":"A metagenomic library cloning strategy that promotes high-level expression of captured genes to enable efficient functional screening","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; AbCellera (Canada); University of British Columbia","funders":"Marsden Fund; National Institutes of Health; Health Research Council of New Zealand; Royal Society Te Apārangi; Royal Society","keywords":"Nitroreductase; Gene; Biology; Genetics; Computational biology; Plasmid; Metagenomics; Enzyme; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004728761,0.001054603,0.000590267,0.0006802518,0.000266485,0.0005629867,0.0004808561,0.0004902948,0.00194367],"category_scores_gemma":[0.0003414794,0.000376368,0.0006779911,0.0005076583,0.0003483732,0.0002769066,0.0006892926,0.00116962,0.001584621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000414664,"about_ca_system_score_gemma":0.0004257314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004435188,"about_ca_topic_score_gemma":0.0008129153,"domain_scores_codex":[0.99954,0.00007433902,0.00004627459,0.0001130574,0.0001498785,0.00007650637],"domain_scores_gemma":[0.9998112,0.0000397988,0.0000366203,0.0000522143,0.00002735681,0.00003281768],"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":[0.00001834721,0.00001143634,0.00005587507,0.00002260069,0.00000476807,0.00002726944,0.000007464077,0.0000483546,0.9984121,0.0001227968,0.00005003038,0.001218854],"study_design_scores_gemma":[0.000005070421,0.00007112609,0.0003965239,0.000004319831,0.00001178407,0.0001373444,0.00000796954,0.0005650037,0.9950823,0.00005439621,0.00365925,0.000004970511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5607814,0.001049447,0.4175344,0.0007885761,0.0002842255,0.0009790407,0.006210587,0.004463306,0.007909019],"genre_scores_gemma":[0.7200682,0.001769154,0.2471105,0.0004025619,0.00005685081,0.001031786,0.01168146,0.0011578,0.01672176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00194367,"threshold_uncertainty_score":0.006502271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03422083546276231,"score_gpt":0.2374955103557663,"score_spread":0.203274674893004,"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."}}