{"id":"W4323076270","doi":"10.1093/nargab/lqad017","title":"BaM-seq and TBaM-seq, highly multiplexed and targeted RNA-seq protocols for rapid, low-cost library generation from bacterial samples","year":2023,"lang":"en","type":"article","venue":"NAR Genomics and Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"RNA-Seq; Computational biology; DNA sequencing; Transcriptome; RNA; Genomic library; Deep sequencing; Biology; Computer science; Gene; Gene expression; Genetics; Genome; Base sequence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002536088,0.001664912,0.001405914,0.001789634,0.001141566,0.001729859,0.00188673,0.001240807,0.005066273],"category_scores_gemma":[0.002272081,0.001273826,0.001240953,0.001704873,0.0007971188,0.001336384,0.001637334,0.002826435,0.005618275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007779725,"about_ca_system_score_gemma":0.001511816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006317373,"about_ca_topic_score_gemma":0.002161594,"domain_scores_codex":[0.9969868,0.0007174875,0.0002632061,0.0006557885,0.001158284,0.0002183444],"domain_scores_gemma":[0.9990746,0.0002725147,0.0001832058,0.000189465,0.0001951863,0.00008506879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001582222,0.00007311557,0.0004113847,0.0008721653,0.00006517475,0.00009683635,0.00008908111,0.0007487473,0.9581376,0.002355138,0.00399663,0.03299578],"study_design_scores_gemma":[0.00003752661,0.0002005002,0.001780829,0.0001023998,0.00006665095,0.0005156941,0.00005756492,0.007809398,0.900325,0.002367475,0.0866412,0.00009566886],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03095503,0.006768635,0.9343514,0.0005263647,0.0007432292,0.001246358,0.01023767,0.009131054,0.006040239],"genre_scores_gemma":[0.04635984,0.004844915,0.9191062,0.0008165882,0.000192404,0.003330036,0.01688957,0.001255214,0.007205178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005066273,"threshold_uncertainty_score":0.0169484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278299997355659,"score_gpt":0.2409403088289274,"score_spread":0.2131103090933615,"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."}}