{"id":"W1994896760","doi":"10.1016/j.jbiotec.2013.04.004","title":"Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest","year":2013,"lang":"en","type":"article","venue":"Journal of Biotechnology","topic":"Plant biochemistry and biosynthesis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":248,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of British Columbia; Canada's Michael Smith Genome Sciences Centre; Saskatchewan Research Council (Canada); Brock University; University of Calgary","funders":"Genome Alberta; Genome British Columbia; Ontario Ministry of Research and Innovation; Government of Alberta; Genome Prairie; McGill University; Génome Québec; Genome Canada","keywords":"RefSeq; KEGG; De novo transcriptome assembly; Computational biology; Sequence assembly; Biology; Genome; Annotation; Transcriptome; DNA sequencing; UniProt; Gene; Computer science; Bioinformatics; Genetics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000253146,0.0002169975,0.0004149665,0.0004255762,0.000327085,0.0003701711,0.0001669292,0.0001751372,0.0004199697],"category_scores_gemma":[0.000252255,0.00008505758,0.0004882665,0.0006709059,0.0001565871,0.0001725031,0.0002149332,0.0003323288,0.0002659086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003152238,"about_ca_system_score_gemma":0.000395447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001370667,"about_ca_topic_score_gemma":0.001820485,"domain_scores_codex":[0.9998689,0.00002682892,0.00001200745,0.00003458701,0.00003526753,0.00002232912],"domain_scores_gemma":[0.9999071,0.00003177593,0.00001175282,0.00001691001,0.00001660021,0.00001577286],"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":[0.00007755042,0.00001918891,0.0006739474,0.00005301533,0.000007566166,0.00004371744,0.00002853759,0.0003835191,0.9953563,0.00009903169,0.00004969028,0.003207874],"study_design_scores_gemma":[0.00004161714,0.0005519492,0.1356782,0.00003712086,0.0001916111,0.0005858286,0.0002466083,0.02068972,0.8250607,0.001077416,0.01580708,0.00003215497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9285202,0.001428356,0.05727653,0.00008837266,0.00002811901,0.0002051039,0.00943004,0.0003775081,0.002645814],"genre_scores_gemma":[0.8334744,0.002925388,0.1176344,0.0001743975,0.00001784834,0.0005148525,0.04260914,0.0001909469,0.002458512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001370667,"threshold_uncertainty_score":0.002725363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04879625593774196,"score_gpt":0.2524135058831938,"score_spread":0.2036172499454518,"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."}}