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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Main Authors: 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
Format: Article
Language:English
Published: BMC 2020-01-01
Series:BMC Genomics
Subjects:
RNA
HSF
Online Access:https://doi.org/10.1186/s12864-020-6484-5
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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
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spelling 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