{"id":"W2796126220","doi":"10.1038/s41598-018-23978-z","title":"IMSindel: An accurate intermediate-size indel detection tool incorporating de novo assembly and gapped global-local alignment with split read analysis","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Core Research for Evolutional Science and Technology; Ichiro Kanehara Foundation for the Promotion of Medical Sciences and Medical Care; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Indel; INDEL Mutation; Frameshift mutation; Computational biology; Sanger sequencing; DNA sequencing; Computer science; Biology; Genetics; Gene; Mutation; Single-nucleotide polymorphism","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":[0.004588091,0.002305317,0.001697363,0.004069086,0.001118173,0.00191479,0.002337785,0.001369206,0.003856212],"category_scores_gemma":[0.007568912,0.001362907,0.001527404,0.001485142,0.0006778803,0.001798365,0.001843754,0.002028297,0.004403113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008723871,"about_ca_system_score_gemma":0.001522622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001253052,"about_ca_topic_score_gemma":0.003525815,"domain_scores_codex":[0.9974217,0.0005492776,0.0002916641,0.0007615749,0.0008315062,0.0001442519],"domain_scores_gemma":[0.9968323,0.001518172,0.0005993949,0.0004287716,0.0004550707,0.0001662874],"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":[0.002446635,0.0005799055,0.02597498,0.003832504,0.001650978,0.001210972,0.0008787388,0.01630669,0.4762707,0.004563785,0.0527425,0.4135416],"study_design_scores_gemma":[0.0003200071,0.0006501531,0.02094657,0.0002988694,0.0004004051,0.002788567,0.000216059,0.3961414,0.5036876,0.003976285,0.07001004,0.0005640201],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08998614,0.001664558,0.7487916,0.0003111425,0.0002803346,0.0005627151,0.01557254,0.1389137,0.003917218],"genre_scores_gemma":[0.09927886,0.0003260219,0.8744504,0.0002296026,0.00004826364,0.0005580032,0.01725532,0.005414052,0.002439523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004588091,"threshold_uncertainty_score":0.02426445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00913536671114171,"score_gpt":0.2536165155242988,"score_spread":0.2444811488131571,"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."}}