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A CNN - based pipeline for calling somatic SNP and INDEL variants without a matched normal

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DeepSom

DeepSom is a convolutional neural network (CNN) - based pipeline for calling somatic SNP and INDEL variants without a matched normal.

DeepSom can operate in three modes:

  • Inference mode. In this mode, the convolutional neural network (CNN) assigns a pseudoprobability score to each candidate variant which can then be classified as somatic or non-somatic. This is the main operating mode.

  • Test(evaluation) mode. In this mode, we use variants with known labels (somatic/non-somatic) to evaluate DeepSom performance.

  • Train mode. In this mode, the CNN learns relationships between variant tensors and variant labels (somatic/non-somatic). This results in a model which can be used in inference or evaluation.

See dataprep on how to prepare data for the CNN.

See cnn on how to run the CNN.

See test for a test run of DeepSom on a preprocessed set of variants.

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A CNN - based pipeline for calling somatic SNP and INDEL variants without a matched normal

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  • Python 94.8%
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