{"id":"W4401686937","doi":"10.1101/2024.08.16.608306","title":"<i>FindingNemo</i> : A Toolkit for DNA Extraction, Library Preparation and Purification for Ultra Long Nanopore Sequencing","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control","funders":"Biotechnology and Biological Sciences Research Council; Wellcome Trust","keywords":"Nanopore sequencing; Nanopore; DNA extraction; Extraction (chemistry); DNA sequencing; DNA; Computer science; Computational biology; Nanotechnology; Chromatography; Chemistry; Biology; Materials science; Genetics; Polymerase chain reaction; Gene","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":[0.001756275,0.001785301,0.001119999,0.001380064,0.0009493966,0.001730129,0.002549983,0.00121286,0.02764398],"category_scores_gemma":[0.003073046,0.001496103,0.0008835937,0.000884477,0.0009428998,0.001838359,0.003338929,0.002332838,0.03298044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006610101,"about_ca_system_score_gemma":0.001282182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223774,"about_ca_topic_score_gemma":0.002560632,"domain_scores_codex":[0.9980721,0.0002883239,0.0002405969,0.000420707,0.0008161839,0.0001619768],"domain_scores_gemma":[0.9984673,0.0003320149,0.0002016205,0.0006079854,0.0002324277,0.0001586657],"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":[0.0005534448,0.0001854409,0.002189328,0.002165127,0.0002769089,0.0008449329,0.0006007088,0.003705765,0.4923218,0.01916832,0.2865549,0.1914333],"study_design_scores_gemma":[0.00008430448,0.00009573076,0.001888379,0.0002106144,0.00004308776,0.001070425,0.00005593013,0.0204915,0.4835644,0.009525546,0.4827415,0.0002287297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006799861,0.0006712729,0.7839513,0.0007317273,0.0005497455,0.000492957,0.0162345,0.1814155,0.009153085],"genre_scores_gemma":[0.02798647,0.0006705367,0.8741658,0.0007436051,0.0001347134,0.00143955,0.03090839,0.04612485,0.0178261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02764398,"threshold_uncertainty_score":0.09247833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188992476946435,"score_gpt":0.2546922136992518,"score_spread":0.2428022889297874,"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."}}