{"id":"W2606620317","doi":"10.1038/s41598-017-01170-z","title":"Enhancing the throughput and multiplexing capabilities of next generation sequencing for efficient implementation of pooled shRNA and CRISPR screens","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatchewan Cancer Agency; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada; Cancer Research Society; Movember Foundation","keywords":"CRISPR; Computational biology; DNA sequencing; Multiplex; Multiplexing; Small hairpin RNA; Computer science; Heteroduplex; Throughput; Biology; DNA; Genetics; Gene; Telecommunications; RNA; Wireless","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":[0.004447575,0.0009240267,0.00116701,0.001045374,0.0006694994,0.001564224,0.000939999,0.0009593377,0.004133067],"category_scores_gemma":[0.004357801,0.0007959438,0.0008347319,0.0006360336,0.0005825798,0.001134046,0.001231022,0.001868423,0.002137763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006872048,"about_ca_system_score_gemma":0.001053654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006353459,"about_ca_topic_score_gemma":0.003066913,"domain_scores_codex":[0.9964842,0.000892634,0.0002389679,0.0006226597,0.001491219,0.0002703294],"domain_scores_gemma":[0.9973272,0.001101832,0.0002813384,0.0004610665,0.0005778654,0.0002508062],"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":[0.00009009743,0.00004764448,0.0004323038,0.0001851705,0.00004065481,0.00007610212,0.00008591937,0.001125109,0.9792063,0.001095245,0.0007655347,0.01684989],"study_design_scores_gemma":[0.00002026206,0.0001665374,0.001638055,0.00002703049,0.00005283151,0.000269297,0.00002859542,0.01269915,0.9706728,0.0008006088,0.01357996,0.0000448285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08865164,0.0008151281,0.8995416,0.000453326,0.000218302,0.0005856716,0.001600437,0.004505783,0.003628054],"genre_scores_gemma":[0.1333134,0.0008921831,0.8572036,0.0003253464,0.00007367045,0.0009880253,0.002266712,0.0008404653,0.004096648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004447575,"threshold_uncertainty_score":0.0235213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03423672682300041,"score_gpt":0.3381486888757322,"score_spread":0.3039119620527319,"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."}}