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Kipoi: accelerating the community exchange and reuse of predictive models for genomics

View ORCID ProfileŽiga Avsec, Roman Kreuzhuber, View ORCID ProfileJohnny Israeli, Nancy Xu, Jun Cheng, Avanti Shrikumar, Abhimanyu Banerjee, Daniel S. Kim, View ORCID ProfileLara Urban, View ORCID ProfileAnshul Kundaje, View ORCID ProfileOliver Stegle, View ORCID ProfileJulien Gagneur
doi: https://doi.org/10.1101/375345
Žiga Avsec
1Technical University Munich
2QBM Graduate School, Ludwig-Maximilians Universität, Munich
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  • ORCID record for Žiga Avsec
  • For correspondence: avsec@in.tum.de akundaje@stanford.edu oliver.stegle@embl.de gagneur@in.tum.de
Roman Kreuzhuber
3Department of Haematology, University of Cambridge
4European Molecular Biology Laboratory, European Bioinformatics Institute
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Johnny Israeli
5Stanford University
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Nancy Xu
5Stanford University
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Jun Cheng
1Technical University Munich
2QBM Graduate School, Ludwig-Maximilians Universität, Munich
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Avanti Shrikumar
5Stanford University
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Abhimanyu Banerjee
5Stanford University
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Daniel S. Kim
5Stanford University
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Lara Urban
4European Molecular Biology Laboratory, European Bioinformatics Institute
6European Molecular Biology Laboratory, Genome Biology Unit
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  • ORCID record for Lara Urban
Anshul Kundaje
5Stanford University
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  • ORCID record for Anshul Kundaje
  • For correspondence: avsec@in.tum.de akundaje@stanford.edu oliver.stegle@embl.de gagneur@in.tum.de
Oliver Stegle
4European Molecular Biology Laboratory, European Bioinformatics Institute
6European Molecular Biology Laboratory, Genome Biology Unit
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  • ORCID record for Oliver Stegle
  • For correspondence: avsec@in.tum.de akundaje@stanford.edu oliver.stegle@embl.de gagneur@in.tum.de
Julien Gagneur
1Technical University Munich
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  • For correspondence: avsec@in.tum.de akundaje@stanford.edu oliver.stegle@embl.de gagneur@in.tum.de
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Abstract

Advanced machine learning models applied to large-scale genomics datasets hold the promise to be major drivers for genome science. Once trained, such models can serve as a tool to probe the relationships between data modalities, including the effect of genetic variants on phenotype. However, lack of standardization and limited accessibility of trained models have hampered their impact in practice. To address this, we present Kipoi, a collaborative initiative to define standards and to foster reuse of trained models in genomics. Already, the Kipoi repository contains over 2,000 trained models that cover canonical prediction tasks in transcriptional and post-transcriptional gene regulation. The Kipoi model standard grants automated software installation and provides unified interfaces to apply and interpret models. We illustrate Kipoi through canonical use cases, including model benchmarking, transfer learning, variant effect prediction, and building new models from existing ones. By providing a unified framework to archive, share, access, use, and build on models developed by the community, Kipoi will foster the dissemination and use of machine learning models in genomics.

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Posted July 24, 2018.
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Kipoi: accelerating the community exchange and reuse of predictive models for genomics
Žiga Avsec, Roman Kreuzhuber, Johnny Israeli, Nancy Xu, Jun Cheng, Avanti Shrikumar, Abhimanyu Banerjee, Daniel S. Kim, Lara Urban, Anshul Kundaje, Oliver Stegle, Julien Gagneur
bioRxiv 375345; doi: https://doi.org/10.1101/375345
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Kipoi: accelerating the community exchange and reuse of predictive models for genomics
Žiga Avsec, Roman Kreuzhuber, Johnny Israeli, Nancy Xu, Jun Cheng, Avanti Shrikumar, Abhimanyu Banerjee, Daniel S. Kim, Lara Urban, Anshul Kundaje, Oliver Stegle, Julien Gagneur
bioRxiv 375345; doi: https://doi.org/10.1101/375345

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