MinCall - MinION end2end convolutional deep learning basecaller
The Oxford Nanopore Technologies's MinION is the first portable DNA sequencing device. It is capable of producing long reads, over 100 kBp were reported. However, it has significantly higher error rate than other methods. In this study, we present MinCall, an end2end basecaller model for the MinION. The model is based on deep learning and uses convolutional neural networks (CNN) in its implementation. For extra performance, it uses cutting edge deep learning techniques and architectures, batch normalization and Connectionist Temporal Classification (CTC) loss. The best performing deep learning model achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and 1D reads.
Code (1)
Tasks
Deep LearningGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SquiggleFilter: An Accelerator for Portable Virus Detection
The MinION is a recent-to-market handheld nanopore sequencer. It can be used to determine the whole genome of a target virus in a biological sample. Its Read Until feature allows us to skip sequencing a majority of non-t…
Dynamic Time WarpingGPUOptimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
Convolutional neural network is a machine-learning model widely applied in various prediction tasks, such as computer vision and medical image analysis. Their great predictive power requires extensive computation, which …
Medical Image AnalysisPredictionPrivacy PreservingTripletDominion: A New Frontier for AI Research
In recent years, machine learning approaches have made dramatic advances, reaching superhuman performance in Go, Atari, and poker variants. These games, and others before them, have served not only as a testbed but have …
Reinforcement Learning (RL)TargetCall: Eliminating the Wasted Computation in Basecalling via Pre-Basecalling Filtering
Basecalling is an essential step in nanopore sequencing analysis where the raw signals of nanopore sequencers are converted into nucleotide sequences, i.e., reads. State-of-the-art basecallers employ complex deep learnin…
Nanopore Base Calling on the Edge
We developed a new base caller DeepNano-coral for nanopore sequencing, which is optimized to run on the Coral Edge Tensor Processing Unit, a small USB-attached hardware accelerator. To achieve this goal, we have designed…
speech-recognitionSpeech Recognition