Deepsimulator method and system for mimicking nanopore sequencing

A computing device for sequencing biopolymers, the computing device comprising:

• a processor for,
• selecting with a sequence generator module an input nucleotide sequence having plural k-mers;
• simulating with a deep learning simulator, actual electrical current signals corresponding to the input nucleotide sequence;
• and
• identifying reads that correspond to the actual electrical current signals;
• and
• a display for displaying the reads,
• wherein the deep learning simulator includes a context-dependent deep learning model that takes into consideration a position of a k-mer of the plural k-mers on the input nucleotide sequence when calculating a corresponding actual electrical current, and
• wherein the context-dependent deep learning model calculates transformed signals by using a Bi-LSTM extended Deep Canonical Time Warping, which combines a bi-directional long short-term memory (Bi-LSTM) method with a deep canonical time warping (DCTW) method.

A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, implement instructions for sequencing biopolymers, the instructions comprising:

• selecting with a sequence generator module an input nucleotide sequence having plural k-mers;
• simulating with a deep learning simulator, actual electrical current signals corresponding to the input nucleotide sequence;
• identifying reads that correspond to the actual electrical current signals;
• and
• instructing a display to display the reads,
• wherein the deep learning simulator includes a context-dependent deep learning model that takes into consideration a position of a k-mer of the plural k-mers on the input nucleotide sequence when calculating a corresponding actual electrical current, and
• wherein the context-dependent deep learning model calculates transformed signals by using a Bi-LSTM extended Deep Canonical Time Warping, which combines a bi-directional long short-term memory (Bi-LSTM) method with a deep canonical time warping (DCTW) method.