Introduction Advancing whole-genome precision medicine requires understanding how gene expression is altered by genetic variants, especially those that are outside of protein-coding regions. We developed a computational technique that scores how strongly genetic variants alter RNA splicing, a critical step in gene expression whose disruption contributes to many diseases, including cancers and neurological disorders. A genome-wide analysis reveals tens of thousands of variants that alter splicing and are enriched with a wide range of known diseases. Our results provide insight into the genetic basis of spinal muscular atrophy, hereditary nonpolyposis colorectal cancer and autism spectrum disorder. Methods We used machine learning to derive a computational model that takes as input DNA sequences and applies general rules to predict splicing in human tissues. Given a test variant, our model computes a score that predicts how much the variant disrupts splicing. The model was derived in such a way that it can be used to study diverse diseases and disorders, and to determine the consequences of common, rare, and even spontaneous variants. Results Our technique is able to accurately classify disease-causing variants and provides insights into the role of aberrant splicing in disease. We scored over 650,000 DNA variants and found that disease-causing variants have higher scores than common variants and even those associated with disease in genome-wide association studies. Our model predicts substantial and unexpected aberrant splicing due to variants within introns and exons, including those far from the splice site. For example, among intronic variants that are more than 30 nucleotides away from a splice site, known disease variants alter splicing nine times more often than common variants; among missense exonic disease variants, those that least impact protein function are over five times more likely to alter splicing than other variants. Autism has been associated with disrupted splicing in brain regions, so we used our method to score variants detected using whole genome sequencing data from individuals with and without autism. Genes with high scoring variants include many that have been previously linked with autism, as well as new genes with known neurodevelopmental phenotypes. Most of the high scoring variants are intronic and cannot be detected by exome analysis techniques. When we score clinical variants in spinal muscular atrophy and colorectal cancer genes, up to 94% of variants found to disrupt splicing using minigene reporters are correctly classified. Discussion In the context of precision medicine, causal support for variants that is independent of existing studies is greatly needed. Our computational model was trained to predict splicing from DNA sequence alone, without using disease annotations or population data. Consequently, its predictions are independent of and complementary to population data, genome-wide association studies (GWAS), expression-based quantitative trait loci (QTL), and functi...

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Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of -regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5' UTRs as well as native 5' UTRs. The model additionally was used to computationally evolve highly active 5' UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.

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