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Training Genotype Callers with Neural Networks

Remi Torracinta, View ORCID ProfileFabien Campagne
doi: https://doi.org/10.1101/097469
Remi Torracinta
1The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, United States of America
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Fabien Campagne
1The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY, United States of America
2Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, United States of America
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  • ORCID record for Fabien Campagne
  • For correspondence: fac2003@campagnelab.org
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ABSTRACT

We present an open source software toolkit for training deep learning models to call genotypes in high-throughput sequencing data. The software supports SAM, BAM, CRAM and Goby alignments and the training of models for a variety of experimental assays and analysis protocols. We evaluate this software in the Illumina Platinum whole genome datasets and find that a deep learning model trained on 80% of the genome achieves a 0.986% accuracy on variants (genotype concordance) when trained with 10% of the data from a genome. The software is distributed at https://github.com/CampagneLaboratory/variationanalysis. The software makes it possible to train genotype calling models on consumer hardware with CPUs or GPU(s). It will enable individual investigators and small laboratories to train and evaluate their own models and to make open source contributions. We welcome contributions to extend this early prototype or evaluate its performance on other gold standard datasets.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 30, 2016.
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Training Genotype Callers with Neural Networks
Remi Torracinta, Fabien Campagne
bioRxiv 097469; doi: https://doi.org/10.1101/097469
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Training Genotype Callers with Neural Networks
Remi Torracinta, Fabien Campagne
bioRxiv 097469; doi: https://doi.org/10.1101/097469

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