ABSTRACT
Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.
Subject(s)
Genetic Variation , Primates , Animals , Humans , Base Sequence , Gene Frequency , Primates/genetics , Whole Genome SequencingABSTRACT
Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.
ABSTRACT
Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.
ABSTRACT
We present an atrous convolutional encoder-decoder trained to denoise electron micrographs. It consists of a modified Xception backbone, atrous convoltional spatial pyramid pooling module and a multi-stage decoder. Our neural network was trained end-to-end using 512â¯â¯×â¯â¯512 micrographs created from a large dataset of high-dose (â¯>â¯2500 counts per pixel) micrographs with added Poisson noise to emulate low-dose (â¯âªâ¯ 300 counts per pixel) data. It was then fine-tuned for high dose data (200-2500 counts per pixel). Its performance is benchmarked against bilateral, Gaussian, median, total variation, wavelet, and Wiener restoration methods with their default parameters. Our network outperforms their best mean squared error and structural similarity index performances by 24.6% and 9.6% for low doses and by 43.7% and 5.5% for high doses. In both cases, our network's mean squared error has the lowest variance. Source code and links to our high-quality dataset and pre-trained models are available at https://github.com/Jeffrey-Ede/Electron-Micrograph-Denoiser.
