Genetic mutation detection using deep learning
AssigneeNVIDIA Corporation
InventorsJohnny Israeli, Avantika Lal, Michael Vella, Nikolai Yakovenko, Zhen Hu
Life of this patent
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Abstract
The present disclosure provides methods, systems, and computer program products that use deep learning models to classify candidate mutations detected in sequencing data, particularly suboptimal sequencing data. The methods, systems, and programs provide for increased efficiency, accuracy, and speed in identifying mutations from a wide range of sequencing data.
Claims15
A method comprising:
- encoding information representing a candidate mutation, wherein the information comprises absolute location, reference sequence, number of reads (N), and read sequences, wherein the reference sequence and the read sequences are encoded as 2D Boolean matrices and the reference sequence is associated with each of the read sequences;
- and
- processing the encoded information with a deep learning model that jointly processes the reference sequence with each read sequence and classifies the candidate mutation.
A system comprising a processor, a memory device, and a classification engine executable on the processor according to software instructions stored in the memory device, wherein the classification engine is configured to:
- encode information representing a candidate mutation, wherein the encoded information comprises absolute location, reference sequence, number of reads (N), and read sequences, wherein the reference sequence and the read sequences are encoded as 2D Boolean matrices and the reference sequence is associated with each of the read sequences;
- and
- process the encoded information with a deep learning model that jointly processes the reference sequence with each read sequence and classifies the candidate mutation.
A non-transitory computer-readable medium comprising instructions for classifying candidate mutations that, when executed by a processor, cause the processor to perform one or more steps comprising:
- encoding information representing a candidate mutation, wherein the encoded information comprises absolute location, reference sequence, number of reads (N), and read sequences, wherein the reference sequence and the read sequences are encoded as 2D Boolean matrices and the reference sequence is associated with each of the read sequences;
- and
- processing the encoded information with a deep learning model that jointly processes the reference sequence with each read sequence and classifies the candidate mutation.
Dependencies are inferred from the claim text.