دورية أكاديمية
أعرض في EDS

SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.

التفاصيل البيبلوغرافية
العنوان: SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effects on mRNA splicing.
المؤلفون: Leman R; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France.; UNICAEN, Normandie Université, Caen, France., Parfait B; Service de Génétique et Biologie Moléculaires, APHP, HUPC, Hôpital Cochin, Paris, France., Vidaud D; Service de Génétique et Biologie Moléculaires, APHP, HUPC, Hôpital Cochin, Paris, France., Girodon E; Service de Génétique et Biologie Moléculaires, APHP, HUPC, Hôpital Cochin, Paris, France., Pacot L; Service de Génétique et Biologie Moléculaires, APHP, HUPC, Hôpital Cochin, Paris, France., Le Gac G; Inserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne Occidentale, Brest, France., Ka C; Inserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne Occidentale, Brest, France., Ferec C; Inserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne Occidentale, Brest, France., Fichou Y; Inserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne Occidentale, Brest, France., Quesnelle C; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France., Aucouturier C; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Muller E; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France., Vaur D; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Castera L; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Boulouard F; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Ricou A; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Tubeuf H; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France.; Integrative Biosoftware, Rouen, France., Soukarieh O; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Gaildrat P; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Riant F; Laboratoire de Génétique, AP-HP, GH Saint-Louis-Lariboisière-Fernand Widal, Paris, France., Guillaud-Bataille M; Département de Biopathologie, Gustave Roussy, Université Paris-Saclay, Villejuif, France., Caputo SM; Department of Genetics, Institut Curie, Paris Sciences Lettres Research University, Paris, France., Caux-Moncoutier V; Department of Genetics, Institut Curie, Paris Sciences Lettres Research University, Paris, France., Boutry-Kryza N; Unité Mixte de Génétique Constitutionnelle des Cancers Fréquents, Hospices Civils de Lyon, Lyon, France., Bonnet-Dorion F; Departement de Biopathologie Unité de Génétique Constitutionnelle, Institut Bergonie-INSERM U1218, Bordeaux, France., Schultz I; Laboratoire d'Oncogénétique, Centre Paul Strauss, Strasbourg, France., Rossing M; Centre for Genomic Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark., Quenez O; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Goldenberg L; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Harter V; Department of Biostatistics, Baclesse Unicancer Center, Caen, France., Parsons MT; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia., Spurdle AB; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia., Frébourg T; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France.; Department of genetics, Rouen University Hospital, Rouen, France., Martins A; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France., Houdayer C; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France.; Department of genetics, Rouen University Hospital, Rouen, France., Krieger S; Laboratoire de Biologie et Génétique du Cancer, Centre François Baclesse, Caen, France.; Inserm U1245, UNIROUEN, FHU-G4 génomique, Normandie Université, Rouen, France.; UNICAEN, Normandie Université, Caen, France.
المصدر: Human mutation [Hum Mutat] 2022 Dec; Vol. 43 (12), pp. 2308-2323. Date of Electronic Publication: 2022 Nov 20.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9215429 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1098-1004 (Electronic) Linking ISSN: 10597794 NLM ISO Abbreviation: Hum Mutat Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York : Wiley-Liss, c1992-
مواضيع طبية MeSH: RNA Splicing*/genetics , RNA Splice Sites*/genetics, Humans ; Bayes Theorem ; Exons/genetics ; Machine Learning ; Introns/genetics
مستخلص: Modeling splicing is essential for tackling the challenge of variant interpretation as each nucleotide variation can be pathogenic by affecting pre-mRNA splicing via disruption/creation of splicing motifs such as 5'/3' splice sites, branch sites, or splicing regulatory elements. Unfortunately, most in silico tools focus on a specific type of splicing motif, which is why we developed the Splicing Prediction Pipeline (SPiP) to perform, in one single bioinformatic analysis based on a machine learning approach, a comprehensive assessment of the variant effect on different splicing motifs. We gathered a curated set of 4616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. The Bayesian analysis provided us with the number of control variants, that is, variants without impact on splicing, to mimic the deluge of variants from high-throughput sequencing data. Results show that SPiP can deal with the diversity of splicing alterations, with 83.13% sensitivity and 99% specificity to detect spliceogenic variants. Overall performance as measured by area under the receiving operator curve was 0.986, better than SpliceAI and SQUIRLS (0.965 and 0.766) for the same data set. SPiP lends itself to a unique suite for comprehensive prediction of spliceogenicity in the genomic medicine era. SPiP is available at: https://sourceforge.net/projects/splicing-prediction-pipeline/.
(© 2022 The Authors. Human Mutation published by Wiley Periodicals LLC.)
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فهرسة مساهمة: Keywords: RNA; SPiP; machine learning; sequence variants; splicing predictions
المشرفين على المادة: 0 (RNA Splice Sites)
تواريخ الأحداث: Date Created: 20221023 Date Completed: 20221214 Latest Revision: 20240320
رمز التحديث: 20240320
مُعرف محوري في PubMed: PMC10946553
DOI: 10.1002/humu.24491
PMID: 36273432
قاعدة البيانات: MEDLINE
الوصف
تدمد:1098-1004
DOI:10.1002/humu.24491