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SPiP: Splicing Prediction Pipeline, a machine learning tool for massive detection of exonic and intronic variant effect on mRNA splicing.
  • +33
  • Raphaël Leman,
  • Béatrice Parfait,
  • Dominique Vidaud,
  • Emmanuelle Girodon,
  • Laurence Pacot,
  • Gérald LE GAC,
  • Chandran Ka,
  • Claude Ferec,
  • Yann Fichou,
  • Céline Quesnelle,
  • Camille Aucouturier,
  • Etienne Muller,
  • Dominique Vaur,
  • Laurent Castera,
  • Flavie Boulouard,
  • Agathe Ricou,
  • Hélène Tubeuf,
  • Omar Soukarieh,
  • Pascaline Gaildrat,
  • Florence Riant,
  • Marine Guillaud-Bataille,
  • Sandrine Caputo,
  • Virginie Moncoutier,
  • Nadia Boutry-Kryza,
  • Françoise Bonnet-Dorion,
  • Ines Schultz,
  • Maria Rossing,
  • Olivier Quenez,
  • Louis Goldenberg,
  • Valentin Harter,
  • Michael Parsons,
  • Amanda Spurdle,
  • Thierry Frébourg,
  • Alexandra Martins,
  • Claude Houdayer,
  • Sophie Krieger
Raphaël Leman
Centre Francois Baclesse Centre de Lutte Contre le Cancer

Corresponding Author:[email protected]

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Béatrice Parfait
Hopital Cochin Service de Radiologie
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Dominique Vidaud
Hopital Cochin Service de Radiologie
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Emmanuelle Girodon
Hopital Cochin Service de Radiologie
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Laurence Pacot
Hopital Cochin Service de Radiologie
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Gérald LE GAC
Universite de Bretagne Occidentale
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Chandran Ka
Universite de Bretagne Occidentale
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Claude Ferec
Universite de Bretagne Occidentale
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Yann Fichou
Universite de Bretagne Occidentale
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Céline Quesnelle
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Camille Aucouturier
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Etienne Muller
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Dominique Vaur
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Laurent Castera
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Flavie Boulouard
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Agathe Ricou
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Hélène Tubeuf
Universite de Rouen
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Omar Soukarieh
Universite de Rouen
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Pascaline Gaildrat
Universite de Rouen
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Florence Riant
Groupe Hospitalier Saint-Louis Lariboisiere et Fernand-Widal
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Marine Guillaud-Bataille
Gustave Roussy
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Sandrine Caputo
Institut Curie Departement d'Oncologie Medicale
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Virginie Moncoutier
Institut Curie Departement d'Oncologie Medicale
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Nadia Boutry-Kryza
Hospices Civils de Lyon
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Françoise Bonnet-Dorion
Institut Bergonie
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Ines Schultz
Centre Paul Strauss
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Maria Rossing
Rigshospitalet
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Olivier Quenez
Universite de Rouen
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Louis Goldenberg
Universite de Rouen
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Valentin Harter
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Michael Parsons
QIMR Berghofer Department of Genetics and Computational Biology
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Amanda Spurdle
QIMR Berghofer Department of Genetics and Computational Biology
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Thierry Frébourg
Universite de Rouen
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Alexandra Martins
Universite de Rouen
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Claude Houdayer
Universite de Rouen
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Sophie Krieger
Centre Francois Baclesse Centre de Lutte Contre le Cancer
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Abstract

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 machine learning approach, comprehensive assessment of variant effect on different splicing motifs. We gathered a curated set of 4,616 variants scattered all along the sequence of 227 genes, with their corresponding splicing studies. Bayesian analysis provided us the number of control variants, i.e. 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, significantly better than 0.965 spliceAI for the same dataset. 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/](https://sourceforge.net/projects/splicing-prediction-pipeline/)
04 Jan 2022Submitted to Human Mutation
05 Jan 2022Submission Checks Completed
05 Jan 2022Assigned to Editor
21 Feb 2022Reviewer(s) Assigned
30 Apr 2022Review(s) Completed, Editorial Evaluation Pending
01 May 2022Editorial Decision: Revise Major
25 May 20221st Revision Received
26 May 2022Submission Checks Completed
26 May 2022Assigned to Editor
31 May 2022Reviewer(s) Assigned
12 Jun 2022Review(s) Completed, Editorial Evaluation Pending
20 Jun 2022Editorial Decision: Revise Minor
10 Oct 20222nd Revision Received
10 Oct 2022Submission Checks Completed
10 Oct 2022Assigned to Editor
17 Oct 2022Review(s) Completed, Editorial Evaluation Pending
18 Oct 2022Editorial Decision: Accept
Dec 2022Published in Human Mutation volume 43 issue 12 on pages 2308-2323. 10.1002/humu.24491