{"id":"W4385658962","doi":"10.2144/btn-2023-0011","title":"A High-throughput Pipeline for DNA/RNA/small RNA Purification from Tissue Samples for Sequencing","year":2023,"lang":"en","type":"article","venue":"BioTechniques","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canada's Michael Smith Genome Sciences Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Genome Canada; Canadian Institutes of Health Research; Genome British Columbia; BC Cancer Foundation","keywords":"RNA; Nucleic acid; Small RNA; DNA; DNA sequencing; Computational biology; Throughput; genomic DNA; Pipeline (software); RNA extraction; Workflow; Chemistry; Molecular biology; Biology; Computer science; Gene; Biochemistry; Database","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":[0.002412523,0.00208007,0.001306627,0.002685002,0.001318667,0.001802841,0.001514091,0.001157311,0.01315672],"category_scores_gemma":[0.002040472,0.001395363,0.001492726,0.001397491,0.000560045,0.001210846,0.001811403,0.00302484,0.01449292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000861625,"about_ca_system_score_gemma":0.003307956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001472524,"about_ca_topic_score_gemma":0.003537923,"domain_scores_codex":[0.9983101,0.0001523292,0.000128133,0.0005859531,0.0006616611,0.0001618102],"domain_scores_gemma":[0.9991129,0.0001836833,0.0000929914,0.0001870302,0.0002909999,0.0001323546],"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":[0.0003701516,0.0001105692,0.001028057,0.0009012072,0.000111351,0.0002692405,0.0002470216,0.001265773,0.8816778,0.002331855,0.02073644,0.09095053],"study_design_scores_gemma":[0.0001212316,0.0004791493,0.005411399,0.0002335325,0.0001516487,0.001178286,0.0001039068,0.01860807,0.7455541,0.004733562,0.2232541,0.0001710406],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0171603,0.002065878,0.9047962,0.0005817887,0.00035924,0.00242468,0.02442521,0.03971152,0.008475182],"genre_scores_gemma":[0.03359565,0.001812511,0.8797085,0.0007448798,0.0001259941,0.003271221,0.06410957,0.005160409,0.01147118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01315672,"threshold_uncertainty_score":0.04401356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0419015871543233,"score_gpt":0.3137303055865678,"score_spread":0.2718287184322445,"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."}}