{"id":"W2135556068","doi":"10.1371/journal.pone.0086039","title":"Comprehensive Transcriptome Assembly of Chickpea (Cicer arietinum L.) Using Sanger and Next Generation Sequencing Platforms: Development and Applications","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Genetic and Environmental Crop Studies","field":"Agricultural and Biological Sciences","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Saskatchewan Research Council (Canada); National Research Council Canada","funders":"Agricultural Research Service; Indo-German Science and Technology Centre; Ministry of Agriculture - Saskatchewan; Consortium of International Agricultural Research Centers; Department of Science and Technology, Ministry of Science and Technology, India; U.S. Department of Agriculture","keywords":"Contig; Transcriptome; Biology; Sanger sequencing; De novo transcriptome assembly; Genetics; Sequence assembly; Genome; Reference genome; Gene; Computational biology; Whole genome sequencing; DNA sequencing; Gene expression","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":[0.0008510306,0.000822442,0.000701028,0.0007946274,0.0007425029,0.0007663183,0.0004199528,0.0004048424,0.001165679],"category_scores_gemma":[0.0003243777,0.0004680259,0.0009420679,0.0008251101,0.0001649702,0.0003677559,0.00061317,0.0007277244,0.00127669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004807379,"about_ca_system_score_gemma":0.001058316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004098074,"about_ca_topic_score_gemma":0.006375663,"domain_scores_codex":[0.9995944,0.00003942784,0.00002920649,0.0001715672,0.0001170737,0.00004843028],"domain_scores_gemma":[0.9997978,0.00002440729,0.00002949916,0.00003966549,0.00007794358,0.0000306548],"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":[0.0001303248,0.00001799087,0.001071104,0.0002272023,0.00003983037,0.0001143199,0.0001412643,0.0007536233,0.9804783,0.000193576,0.0009820012,0.01585036],"study_design_scores_gemma":[0.00009378637,0.0005484748,0.1136255,0.0001670178,0.000423951,0.001439556,0.0002636296,0.0284358,0.7251946,0.0008671375,0.1287999,0.0001406525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5311559,0.00672145,0.376135,0.0003623762,0.0002117269,0.001289451,0.07010657,0.006492715,0.007524894],"genre_scores_gemma":[0.2526192,0.004056172,0.5416145,0.0003008219,0.00006214575,0.001881408,0.185075,0.001138203,0.01325248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004098074,"threshold_uncertainty_score":0.008148491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1580579365379646,"score_gpt":0.2160245914966311,"score_spread":0.05796665495866657,"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."}}