Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants
Abstract Background Branch points (BPs) map within short motifs upstream of acceptor splice sites (3’ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last...
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2020-01-01
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Series: | BMC Genomics |
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Online Access: | https://doi.org/10.1186/s12864-020-6484-5 |
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Article |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Raphaël Leman Hélène Tubeuf Sabine Raad Isabelle Tournier Céline Derambure Raphaël Lanos Pascaline Gaildrat Gaia Castelain Julie Hauchard Audrey Killian Stéphanie Baert-Desurmont Angelina Legros Nicolas Goardon Céline Quesnelle Agathe Ricou Laurent Castera Dominique Vaur Gérald Le Gac Chandran Ka Yann Fichou Françoise Bonnet-Dorion Nicolas Sevenet Marine Guillaud-Bataille Nadia Boutry-Kryza Inès Schultz Virginie Caux-Moncoutier Maria Rossing Logan C. Walker Amanda B. Spurdle Claude Houdayer Alexandra Martins Sophie Krieger |
spellingShingle |
Raphaël Leman Hélène Tubeuf Sabine Raad Isabelle Tournier Céline Derambure Raphaël Lanos Pascaline Gaildrat Gaia Castelain Julie Hauchard Audrey Killian Stéphanie Baert-Desurmont Angelina Legros Nicolas Goardon Céline Quesnelle Agathe Ricou Laurent Castera Dominique Vaur Gérald Le Gac Chandran Ka Yann Fichou Françoise Bonnet-Dorion Nicolas Sevenet Marine Guillaud-Bataille Nadia Boutry-Kryza Inès Schultz Virginie Caux-Moncoutier Maria Rossing Logan C. Walker Amanda B. Spurdle Claude Houdayer Alexandra Martins Sophie Krieger Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants BMC Genomics Branch point Prediction RNA Benchmark HSF SVM-BPfinder |
author_facet |
Raphaël Leman Hélène Tubeuf Sabine Raad Isabelle Tournier Céline Derambure Raphaël Lanos Pascaline Gaildrat Gaia Castelain Julie Hauchard Audrey Killian Stéphanie Baert-Desurmont Angelina Legros Nicolas Goardon Céline Quesnelle Agathe Ricou Laurent Castera Dominique Vaur Gérald Le Gac Chandran Ka Yann Fichou Françoise Bonnet-Dorion Nicolas Sevenet Marine Guillaud-Bataille Nadia Boutry-Kryza Inès Schultz Virginie Caux-Moncoutier Maria Rossing Logan C. Walker Amanda B. Spurdle Claude Houdayer Alexandra Martins Sophie Krieger |
author_sort |
Raphaël Leman |
title |
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
title_short |
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
title_full |
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
title_fullStr |
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
title_full_unstemmed |
Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
title_sort |
assessment of branch point prediction tools to predict physiological branch points and their alteration by variants |
publisher |
BMC |
series |
BMC Genomics |
issn |
1471-2164 |
publishDate |
2020-01-01 |
description |
Abstract Background Branch points (BPs) map within short motifs upstream of acceptor splice sites (3’ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3’ss. Results We used a large set of constitutive and alternative human 3’ss collected from Ensembl (n = 264,787 3’ss) and from in-house RNAseq experiments (n = 51,986 3’ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3’ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. Conclusions Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3’ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area. |
topic |
Branch point Prediction RNA Benchmark HSF SVM-BPfinder |
url |
https://doi.org/10.1186/s12864-020-6484-5 |
work_keys_str_mv |
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doaj-da6b4502084e4b2a87009788da78c3312021-01-31T16:12:00ZengBMCBMC Genomics1471-21642020-01-0121111210.1186/s12864-020-6484-5Assessment of branch point prediction tools to predict physiological branch points and their alteration by variantsRaphaël Leman0Hélène Tubeuf1Sabine Raad2Isabelle Tournier3Céline Derambure4Raphaël Lanos5Pascaline Gaildrat6Gaia Castelain7Julie Hauchard8Audrey Killian9Stéphanie Baert-Desurmont10Angelina Legros11Nicolas Goardon12Céline Quesnelle13Agathe Ricou14Laurent Castera15Dominique Vaur16Gérald Le Gac17Chandran Ka18Yann Fichou19Françoise Bonnet-Dorion20Nicolas Sevenet21Marine Guillaud-Bataille22Nadia Boutry-Kryza23Inès Schultz24Virginie Caux-Moncoutier25Maria Rossing26Logan C. Walker27Amanda B. Spurdle28Claude Houdayer29Alexandra Martins30Sophie Krieger31Laboratoire de Biologie Clinique et Oncologique, Centre François BaclesseInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseInserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne OccidentaleInserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne OccidentaleInserm UMR1078, Genetics, Functional Genomics and Biotechnology, Université de Bretagne OccidentaleInserm U916, Département de Pathologie, Laboratoire de Génétique Constitutionnelle, Institut BergoniéInserm U916, Département de Pathologie, Laboratoire de Génétique Constitutionnelle, Institut BergoniéService de Génétique, Institut Gustave RoussyLyon Neuroscience Research Center–CRNL, Inserm U1028, CNRS UMR 5292, University of LyonLaboratoire d’Oncogénétique, Centre Paul StraussService de Génétique, Institut CurieCentre for Genomic Medicine, Rigshospitalet, University of CopenhagenDepartment of Pathology and Biomedical Science, University of OtagoDepartment of Genetics and Computational Biology, QIMR Berghofer Medical Research InstituteInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityInserm U1245, Normandy Center for Genomic and Personalized Medicine, Rouen, UNIROUEN, Normandy UniversityLaboratoire de Biologie Clinique et Oncologique, Centre François BaclesseAbstract Background Branch points (BPs) map within short motifs upstream of acceptor splice sites (3’ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3’ss. Results We used a large set of constitutive and alternative human 3’ss collected from Ensembl (n = 264,787 3’ss) and from in-house RNAseq experiments (n = 51,986 3’ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3’ss (99.48 and 65.84% accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17%. Conclusions Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3’ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area.https://doi.org/10.1186/s12864-020-6484-5Branch pointPredictionRNABenchmarkHSFSVM-BPfinder |